The document proposes a methodology to generate context-aware natural language justifications for recommender systems by exploiting distributional semantics models. It involves learning a vector space representation of contexts, identifying the most suitable review excerpts given an item and context, and combining excerpts to form a justification. The goal is to produce justifications that vary based on different consumption contexts and are independent of the underlying recommendation model.
Natural Language Justifications for Recommender Systems Exploiting Text Summa...Cataldo Musto
Natural Language Justifications for Recommender Systems Exploiting Text Summarization and Sentiment Analysis - AI*IA 2019 - Italian Conference on Artificial Intelligence
“Towards Multi-Step Expert Advice for Cognitive Computing” - Dr. Achim Rettin...diannepatricia
Dr. Achim Rettinger from Karlsruhe Institute of Technology presented this today as part of the Cognitive Systems Institute Speaker Series on October 13, 2016
Natural Language Justifications for Recommender Systems Exploiting Text Summa...Cataldo Musto
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“Towards Multi-Step Expert Advice for Cognitive Computing” - Dr. Achim Rettin...diannepatricia
Dr. Achim Rettinger from Karlsruhe Institute of Technology presented this today as part of the Cognitive Systems Institute Speaker Series on October 13, 2016
Laure talked about a very hot topic in the community at the moment with the ChatGPT phenomenon: how to supervise a PhD thesis in NLP in the age of Large Language Models (LLMs)?
Semantic Web & Information Brokering: Opportunities, Commercialization and Ch...Amit Sheth
Amit Sheth, "Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges," Keynote talk at the workshop on Semantic Web: Models, Architecture and Management, September 21, 2000, Lisbon, Portugal.
This was the keynote given at probably the first international event with "Semantic Web" in title (and before the well known SciAm article). As in TBL's use of Semantic Web in his 1999 book, (semantic) metadata plays central role. The use of Worldmodel/Ontology is consistent with our use of ontology for (Web) information integration in 1994 CIKM paper. Summary of the talk by event organizers and other details are at: http://knoesis.org/library/resource.php?id=735
Prof. Sheth started a Semantic Web company Taalee, Inc. in 1999 (product was called MediaAnywhere A/V search engine- discussed in this paper in the context of one of its use by a customer Redband Broadcasting). The product included Semantic Web/populated Ontology based semantic (faceted) search, semantic browsing, semantic personalization, semantic targeting (advertisement), etc as is described in U.S. Patent #6311194, 30 Oct. 2001 (filed 2000). MediaAnywhere has about 25 ontologies in News/Business, Sports, Entertainment, etc.
Taalee merged to become Voquette in 2001 (product was called SCORE), Semagix in 2004 (product was called Semagix Freedom), and then Fortent in 2006 (products included Know Your Customers).
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Schema-agnositc queries over large-schema databases: a distributional semanti...Andre Freitas
The evolution of data environments towards the growth in the size, complexity, dy-
namicity and decentralisation (SCoDD) of schemas drastically impacts contemporary
data management. The SCoDD trend emerges as a central data management concern
in Big Data scenarios, where users and applications have a demand for more complete
data, produced by independent data sources, under different semantic assumptions and
contexts of use. Most Database Management Systems (DBMSs) today target a closed
communication scenario, where the symbolic schema of the database is known a priori
by the database user, which is able to interpret it in an unambiguous way. The context
in which the data is consumed and produced is well-defined and it is typically the
same context in which the data was created. In contrast, data management under the
SCoDD conditions target an open communication scenario where the symbolic system of
the database is unknown by the user and multiple interpretation contexts are possible.
In this case the database can be created under a different context from the database
user. The emergence of this new data environment demands the revisit of the semantic
assumptions behind databases and the design of data access mechanisms which can
support semantically heterogeneous (open communication) data environments.
This work aims at filling this gap by proposing a complementary semantic model for
databases, based on distributional semantic models. Distributional semantics provides a
complementary perspective to the formal perspective of database semantics, which supports
semantic approximation as a first-class database operation. Differently from models
which describe uncertain and incomplete data or probabilistic databases, distributional-
relational models focuses on the construction of conceptual approximation approaches
for databases, supported by a comprehensive semantic model automatically built from
large-scale unstructured data external to the database, which serves as a semantic/com-
monsense knowledge base. The semantic model can be used to support schema-agnosticqueries, i.e. abstracting the data consumer from a specific conceptualization behind the
data.
The proposed distributional-relational semantic model is supported by a distributional
structured vector space model, named τ −Space, which represents structured data under
a distributional semantic model representation which, in coordination with a query plan-
ning approach, supports a schema-agnostic query mechanism for large-schema databases.
The query mechanism is materialized in the Treo query engine and is evaluated using
schema-agnostic natural language queries.
The evaluation of the query mechanism confirms that distributional semantics provides
a high-recall, medium-high precision, and low maintainability solution to cope with
the abstraction and conceptual-level differences in schema-agnostic queries over largeschema/
schema-less open domain dataset
A Hybrid Method of Long Short-Term Memory and AutoEncoder Architectures for S...AhmedAdilNafea
Sarcasm detection is considered one of the most challenging tasks in sentiment analysis and opinion mining applications in the social media. Sarcasm identification is therefore essential for a good public opinion decision. There are some studies on sarcasm detection that apply standard word2vec model and have shown great performance with word-level analysis. However, once a sequence of terms is being tackled, the performance drops. This is because averaging the embedding of each term in a sentence to get the general embedding would discard the important embedding of some terms. LSTM showed significant improvement in terms of document embedding. However, within the classification LSTM requires adding additional information in order to precisely classify the document into sarcasm or not. This study aims to propose two technique based on LSTM and Auto-Encoder for improving the sarcasm detection. A benchmark dataset has been used in the experiments along with several pre-processing operations that have been applied. These include stop word removal, tokenization and special character removal with LSTM which can be represented by configuring the document embedding and using Auto-Encoder the classifier that was trained on the proposed LSTM. Results showed that the proposed LSTM with Auto-Encoder outperformed the baseline by achieving 84% of f-measure for the dataset. The main reason behind the superiority is that the proposed auto encoder is processing the document embedding as input and attempt to output the same embedding vector. This will enable the architecture to learn the interesting embedding that have significant impact on sarcasm polarity.
How can text-mining leverage developments in Deep Learning? Presentation at ...jcscholtes
How can text-mining leverage developments in Deep Learning?
Text-mining focusses primary on extracting complex patterns from unstructured electronic data sets and applying machine learning for document classification. During the last decade, a generation of efficient and successful algorithms has been developed using bag-of-words models to represent document content and statistical and geometrical machine learning algorithms such as Conditional Random Fields and Support Vector Machines. These algorithms require relatively little training data and are fast on modern hardware. However, performance seems to be stuck around 90% F1 values.
In computer vision, deep learning has shown great success where the 90% barrier has been broken in many application. In addition, deep learning also shows new successes for transfer learning and self-learning such as reinforcement leaning. Dedicated hardware helped us to overcome computational challenges and methods such as training data augmentation solved the need for unrealistically large data sets.
So, it would make sense to apply deep learning also on textual data as well. But how do we represent textual data: there are many different methods for word embeddings and as many deep learning architectures. Training data augmentation, transfer learning and reinforcement leaning are not fully defined for textual data.
Real-time Generation of Topic Maps from Speech Streamstmra
Topic Maps are means for representing sophisticated indexes of any information collections for the purpose of semantic information. The creation of Topic Maps bases on a theoretic fundament which is introduced in this paper. Moreover, the Observation Principle is the result of a deep investigation of the Subject Equality Decision Chain will be discussed as well as the Semantic Talk System which generates sophisticated, conceptual indexes of speech streams in realtime. This paper describes how these indexes are created, how they are represented as Topic Maps and how they can be used for integration purposes.
Towards a Distributional Semantic Web StackAndre Freitas
The ability of distributional semantic models (DSMs) to dis-
cover similarities over large scale heterogeneous and poorly structured data brings them as a promising universal and low-effort framework to support semantic approximation and knowledge discovery. This position paper explores the role of distributional semantics in the Semantic Web vision, based on the state-of-the-art distributional-relational models, categorizing and generalizing existing approaches into a Distributional Semantic Web stack.
Intelligenza Artificiale e Social Media - Monitoraggio della Farnesina e La M...Cataldo Musto
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Laure talked about a very hot topic in the community at the moment with the ChatGPT phenomenon: how to supervise a PhD thesis in NLP in the age of Large Language Models (LLMs)?
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Taalee merged to become Voquette in 2001 (product was called SCORE), Semagix in 2004 (product was called Semagix Freedom), and then Fortent in 2006 (products included Know Your Customers).
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Schema-agnositc queries over large-schema databases: a distributional semanti...Andre Freitas
The evolution of data environments towards the growth in the size, complexity, dy-
namicity and decentralisation (SCoDD) of schemas drastically impacts contemporary
data management. The SCoDD trend emerges as a central data management concern
in Big Data scenarios, where users and applications have a demand for more complete
data, produced by independent data sources, under different semantic assumptions and
contexts of use. Most Database Management Systems (DBMSs) today target a closed
communication scenario, where the symbolic schema of the database is known a priori
by the database user, which is able to interpret it in an unambiguous way. The context
in which the data is consumed and produced is well-defined and it is typically the
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SCoDD conditions target an open communication scenario where the symbolic system of
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In this case the database can be created under a different context from the database
user. The emergence of this new data environment demands the revisit of the semantic
assumptions behind databases and the design of data access mechanisms which can
support semantically heterogeneous (open communication) data environments.
This work aims at filling this gap by proposing a complementary semantic model for
databases, based on distributional semantic models. Distributional semantics provides a
complementary perspective to the formal perspective of database semantics, which supports
semantic approximation as a first-class database operation. Differently from models
which describe uncertain and incomplete data or probabilistic databases, distributional-
relational models focuses on the construction of conceptual approximation approaches
for databases, supported by a comprehensive semantic model automatically built from
large-scale unstructured data external to the database, which serves as a semantic/com-
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The query mechanism is materialized in the Treo query engine and is evaluated using
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Exploiting Distributional Semantics Models for Natural Language Context-aware Justifications for Recommender Systems
1. @cataldomusto
cataldo.musto@uniba.it
Exploiting Distributional Semantics Models
for Natural Language Context-aware
Justifications for Recommender Systems
CATALDO MUSTO, GIUSEPPE SPILLO, PASQUALE LOPS, MARCO DE GEMMIS, GIOVANNI SEMERARO
UNIVERSITÀ DEGLI STUDI DI BARI ‘ALDO MORO’ – ITALY
SWAP RESEARCH GROUP – HTTP://WWW.DI.UNIBA.IT/~SWAP
IntRS 2020 – Joint Workshop on
Interfaces and Human Decision-Making
for Recommender Systems
jointly held with ACM RecSys 2020
Online - Worldwide– September 26, 2020
2. The Explanation Problem
Recommendation
2Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Profile
3. A solution: review-based features
To identify relevant and distinguishing
characteristics of the recommended
item by mining users’ reviews
3Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Cataldo Musto, Pasquale Lops, Marco de Gemmis, Giovanni
Semeraro. Justifying Recommendations through Aspect-based
Sentiment Analysis of Users Reviews. UMAP 2019: 4-12
4. A solution: review-based features
To identify relevant and distinguishing
characteristics of the recommended
item by mining users’ reviews
4Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
funny yarn
memorable writing
interesting concept
romantic end….
5. A solution: review-based features
I recommend you Stranger Than
Fiction because people who liked the
movie think that it has a memorable
writing. Moreover, people liked
Stranger Than Fiction since it has a
romantic end.
5Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
6. Context plays a key role for
decision-making tasks
• Contextual factors (mood, company) do
influence the selection of the most
suitable item to be recommended;
6
…What about the context?
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
SHALL AN EXPLANATION BE
INFLUENCED BY THE CONTEXT OF
CONSUMPTION?
7. A Methodology to
Generate Context-aware
Post-Hoc Natural Language
Justifications Exploiting
Distributional Semantics
Models
7
Contribution
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
8. A Methodology to
Generate Context-aware
Post-Hoc Natural Language
Justifications Exploiting
Distributional Semantics
Models
8
Contribution - Hallmarks
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Justifications vary
depending on the different
contexts of consumption
Justifications are
independent of the
underlying recommendation
model
Justifications are generated
by exploiting a geometrical
representation of items,
contexts and sentences
9. 9
Workflow
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
10. 10
Workflow
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Step 1
We learn a vector-space representation of ‘contexts’
11. 11
Workflow
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Step 2
We identify the most suitable review excerpts,
given an item and a vector-space representation
of ‘contexts’
12. 12
Workflow
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Step 3
We put together the review excerpts, to
generate the final context-aware justification
13. 13
Context Learner
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Step 1
14. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
• Goal: to build a ‘representation’ of the contexts
• Intuition: to exploit Distributional Semantics Models (DSMs) to
obtain a vector space representation of each context
14
Context Learner
content representation
company= friends
company= colleagues
company= family
15. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020 15
Distributional Semantics Models
Ludwig Wittgenstein
(Austrian philosopher)
Meaning of a word is
determined by its usage.
«Words that share a similar context
have a similar meaning»
16. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020 16
Distributional Semantics Models
Ludwig Wittgenstein
(Austrian philosopher)
Recent techniques to represent
textual content (Word2Vec,
BERT, etc) are all inspired by
distributional hypothesis.
«Words that share a similar context
have a similar meaning»
17. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Distributional Semantics Models
A vector space representation of each word based
on word usage can be obtained
17
beer
wine
glass
spoon
This is called
WordSpace
18. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Distributional Semantics Models
c1 c2 c3 c4 c5 c6 c7 c8 c9
beer ✔ ✔ ✔ ✔
wine ✔ ✔ ✔ ✔ ✔
spoon ✔ ✔ ✔ ✔
glass ✔ ✔ ✔ ✔ ✔
Representation based on a term-context
matrix encoding term usage.
18
19. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Distributional Semantics Models
Good overlap = similar meaning
Each row of the matrix is a vector
19
c1 c2 c3 c4 c5 c6 c7 c8 c9
beer ✔ ✔ ✔ ✔
wine ✔ ✔ ✔ ✔ ✔
spoon ✔ ✔ ✔ ✔
glass ✔ ✔ ✔ ✔ ✔
20. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
• Question: how can we exploit DSMs for our goals?
We designed the following pipeline
1. Contexts Definition
2. Sentence Annotation
3. Vector Space Construction
4. Output Generation
20
Context Learner
21. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
1. Contexts Definition
◦ We manually define contextual factors and contextual dimensions for
a specific domain (e.g., movie recommendation)
21
Context Learner
Attention Company Mood
High AttentionLow Attention Family PartnerFriends Bad Mood Good Mood
22. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
2. Sentence Annotation
◦ To build a representation of each context, we need to manually
annotate sentences (e.g., reviews excerpts) with the set of contexts in
which they are suitable as context-aware justifications.
22
Context Learner
Not easy to understand, requires a very careful vision’
A fairy tale, pleasant and enchanting
A very romantic movie
(…repeat over many sentences)
23. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
3. Vector Space Construction
◦ Once the annotation step is completed, we tokenize sentences
◦ We build a term-context matrix encoding term usage (as in DSMs)
23
Context Learner
careful ✔✔ ✔
fairy ✔✔ ✔
romantic ✔✔
intense ✔
easy ✔ ✔
24. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
3. Vector Space Construction
◦ It is important to emphasize that we are not limited to single word.
Rows of the matrix can be also bigrams, as well.
24
Context Learner
careful vision ✔ ✔
fairy tale ✔ ✔
romantic
movie
✔
intense plot ✔
easy vision ✔ ✔
25. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
4. Output Generation
◦ Column Vectors = Vector Space Representation of Each Context
◦ Lexicons = top-k lemmas with the highest score in a column
25
Context Learner
careful ✔✔ ✔
fairy ✔✔ ✔
romantic ✔✔
intense ✔
easy ✔ ✔
26. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
4. Output Generation
◦ Column Vectors = Vector Space Representation of Each Context
◦ Lexicons = top-k lemmas with the highest score in a column
26
Context Learner
= { fairy, calm, story, kids … }
= { atmoshpere, romantic, … }
= { funny, simple, smooth … }
27. 27
Ranker
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Step 2
28. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
• Goal: given an item and context of consumption, to identify the
most suitable review excerpts
• Intuition: to adopt similarity measures in geometrical spaces
28
Ranker
representation
company= friends
company= colleagues
company= family
29. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020 29
Ranker
friends
family
partner
We start from
the output
returned by the
Context Learner
30. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020 30
Ranker
Given a recommended
item, we encode in the
vector space the
available review
excerpts
We limit to sentences
expressing a positive
sentiment
friends
family
partner
‘it is a classy, sweet and funny movie’
‘it has a memorable writing’
‘the movie has a very romantic end’
31. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020 31
Ranker
Next, given a context of
consumption, we
identify the top-K review
excerpts by exploiting
similarity measures in
geometrical spaces
(e.g., cosine similarity)friends
family
partner
‘it is a classy, sweet and funny movie’
‘it has a memorable writing’
‘the movie has a very romantic end’
32. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020 32
Ranker
Next, given a context of
consumption, we
identify the top-K review
excerpts by exploiting
similarity measures in
geometrical spaces
(e.g., cosine similarity)friends
family
partner
‘it is a classy, sweet and funny movie’
33. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020 33
Ranker
Next, given a context of
consumption, we
identify the top-K review
excerpts by exploiting
similarity measures in
geometrical spaces
(e.g., cosine similarity)friends
family
partner
‘the movie has a very romantic end’
34. 34
Generator
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Step 3
35. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
• Goal: to combine the top-k review excerpts in a natural language
justification adapted to the context of consumption
• Intuition: to exploit natural language generation techniques
• Each justification has a fixed part, which is common to all the justifications, and a
dynamic part, which is filled in based on previously identified excerpts.
35
Generator
You should watch ’Stranger than
Fiction’. It is a good movie to
watch with your partner because
it has a very romantic end.
Moreover, plot is very intense.
You should watch ’Stranger than Fiction’.
It is a good movie to watch with your
friends because it crackles with
laughther and pathos and it is a classy
sweet and funny movie.
36. Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020 36
Final Output
You should watch
’Stranger than
Fiction’. It is a good
movie to watch with
your partner
because it has a
very romantic end.
Moreover, plot is
very intense.
You should watch
’Stranger than
Fiction’. It is a good
movie to watch with
your friends because
it crackles with
laughther and pathos
and it is a classy
sweet and funny
movie.
Context-aware Natural Language
Justification based on DSMs
37. Experimental Evaluation
Research Question 1 (RQ1)
How effective are the justifications generated through the pipeline, on varying of different
combinations of the parameters?
Research Question 2 (RQ2)
How does our justifications perform with respect to non-contextual justifications and contextual
justifications based on a fixed lexicon?
Experimental Design
User Study with a Web Application
273 subjects - Movie Domain. 300 movies. ~150k reviews.
Metrics: Transparency, Engagement, Persuasion, Trust, Effectiveness [^]
Parameters: Lexicon (Unigram, Bigrams and Unigram+Bigrams)
Between-subjects for Research Question 1, Within-subjects for Research Question 2
[^] Tintarev, N., & Masthoff, J. Designing and
evaluating explanations for recommender
systems. In Recommender systems
handbook. pp. 479-510. Springer, Boston,
MA. 2011
37Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
38. Experimental Evaluation – WebApp (RQ1)
38Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Welcome
Screen
Context
Selection
39. Experimental Evaluation – WebApp (RQ1)
39Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Generation of
the Justification
Questionnaire
Transparency
Persuasion
Engagement
Trust
40. Experimental Evaluation – WebApp (RQ2)
40Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Comparison
to Baselines
Questionnaire
Transparency
Persuasion
Engagement
Trust
41. Results (Research Question 1)
41
Question Unigrams (Uni) Bigrams (Bi) Uni+Bi
Transparency «I understood why the movie was
suggested to me»
3.38 3.81 3.64
Persuasion «The justification made the
recommendation more convincing»
3.56 3.62 3.54
Engagement «The justification allowed me to discover
more information about the movie»
3.54 3.72 3.70
Trust «The justification increased my trust in
recommender systems»
3.44 3.66 3.61
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
42. Results (Research Question 1)
42
Question Unigrams (Uni) Bigrams (Bi) Uni+Bi
Transparency «I understood why the movie was
suggested to me»
3.38 3.81 3.64
Persuasion «The justification made the
recommendation more convincing»
3.56 3.62 3.54
Engagement «The justification allowed me to discover
more information about the movie»
3.54 3.72 3.70
Trust «The justification increased my trust in
recommender systems»
3.44 3.66 3.61
Intuition: bigrams (e.g., romantic
soundtrack) better catch semantics
of reviews excerpts
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
43. Results (Research Question 2)
MOVIES CA+DSMs Baseline Indifferent
Transparency 52.38% 38.10% 19.32%
Persuasion 54.10% 36.33% 19.57%
Engagement 49.31% 39.23% 11.56%
Trust 42.86% 39.31% 17.83%
43Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Improvement over a non-contextual
baseline based on DSMs
44. Results (Research Question 2)
MOVIES CA+DSMs Baseline Indifferent
Transparency 53.21% 34.47% 12.32%
Persuasion 55.17% 32.33% 12.50%
Engagement 44.51% 32.75% 22.74%
Trust 42.90% 42.11% 14.99%
44Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
Improvement over a contextual
baseline based on a static lexicon
45. Recap
Hallmarks
◦ Diversification of the justification based on the context of consumption
◦ Adoption of DSMs to (unsupervisedly) learn a vector-space representation of context
Contribution
◦ A domain-independent framework to generate post-hoc context-aware review-based
natural language justifications
Findings
◦ A representation based on bigrams better catches the semantics of the different context
of consumptions
◦ Users tend to prefer context-aware justifications, and DSMs allow to build a more
effective representation
45
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
46. Future Work
Generation of personalized
justifications
◦ We aim to encode user preferences into
the generation process
Evaluation of the post-hoc nature
◦ To assess whether the model is solid
enough to ‘explain’ also more complex
and opaque deep learning models
Generation of hybrid justifications
◦ Combining structured features and
review-based features
46
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. Exploiting Distributional Semantics Models for Natural
Language Context-aware Justifications for Recommender Systems. IntRS Workshop@ACM RecSys 2020 – Online – September 26, 2020
RecSys
2021