Slides UMAP'13 paper "Exploiting the Semantic Similarity of Contextual Situations for Pre-filtering Recommendation"
•
1 recomendación•682 vistas
Recomendados
Recomendados
Más contenido relacionado
Similar a Slides UMAP'13 paper "Exploiting the Semantic Similarity of Contextual Situations for Pre-filtering Recommendation"
Similar a Slides UMAP'13 paper "Exploiting the Semantic Similarity of Contextual Situations for Pre-filtering Recommendation" (20)
Último
Último (20)
Slides UMAP'13 paper "Exploiting the Semantic Similarity of Contextual Situations for Pre-filtering Recommendation"
- 1. Exploiting the Semantic Similarity of Contextual Situations for Pre-Filtering Recommendation Victor Codina Francesco Ricci Luigi Ceccaroni UMAP 2013
- 2. UMAP – June 2013, Rome, Italy Outline 2 State of the art Novel contextual pre-filtering approach Evaluation Context-Aware Recommender Systems (CARS)
- 3. UMAP – June 2013, Rome, Italy Context matters 3 Main assumption: items can be experienced differently by the users depending on the current context
- 4. UMAP – June 2013, Rome, Italy Context-Aware Recommender Systems (CARS) 4 CARS are locally adapted prediction models Adapted to the “local” user contextual situation Goal: to find the right level of contextualitzation for a target user and contextual situation Global (all user ratings) vs. Local model (user ratings in context) Difficult task: the optimal level of contextualization depends on several factors Context relevance, enough ratings in context, …
- 5. UMAP – June 2013, Rome, Italy Outline 5 State of the art Novel contextual pre-filtering approach Evaluation Context-Aware Recommender Systems (CARS)
- 6. UMAP – June 2013, Rome, Italy Direct approach that builds a strict local prediction model for each possible contextual situation Only ratings acquired in the target context are used Main limitation: its lack of flexibility Always uses the maximum level of contextualization It produces too narrow prediction models that fail when: Target context is not relevant enough Not enough ratings acquired in the situation (sparsity problem) Exact pre-filtering 6
- 7. UMAP – June 2013, Rome, Italy It exploits context taxonomies to build generalized local models when needed (Adomavicius et al., 2005) Example: Performance limited to the quality of the context model The pre-defined generalization may or may not suit the data Generalized pre-filtering 7
- 8. UMAP – June 2013, Rome, Italy All build a global MF model that integrates contextual information as additional model parameters Which implies that all ratings are used during model learning Two main ways of extending MF with context Tensor Factorization, where context is integrated using high- order MF techniques (Karatzoglou et al., 2010) Context-Aware MF, where context is integrated as contextual biases associated to the items/users (Baltrunas et al., 2011) Main limitations Computation complexity (especially in Tensor Factorization) Potential similarities between situations are not exploited Approaches based on extended Matrix Factorization (MF) 8
- 9. UMAP – June 2013, Rome, Italy Outline 9 State of the art Novel contextual pre-filtering approach Evaluation Context-Aware Recommender Systems (CARS)
- 10. UMAP – June 2013, Rome, Italy Similar idea as in generalized pre-filtering Our approach also builds semantically-related local models Key difference: we use a novel notion of semantic similarities between conditions Based on their effect on the users’ ratings (altering the value) Advantages: No external knowledge is needed (only rating data) Non-conventional (cross-factor) similarities can be found Our solution: semantic pre-filtering 10
- 11. Main assumption: two conditions are similar if they influence the users’ ratings similarly Example (item-based): UMAP – June 2013, Rome, Italy Similarity calculation between conditions based on their implicit semantics (I) 11 Contextual Condition Natural Park 1 Natural Park 2 Natural Park 3 Walking Route 1 Museum 1 Museum 2 sunny - - family - - - rainy -
- 12. To mitigate the sparsity of the co-occurrence matrix we reduce its dimensionality applying SVD Example: UMAP – June 2013, Rome, Italy Similarity calculation between conditions based on their implicit semantics (II) 12 Contextual Condition Latent Feature 1 Latent Feature 2 Latent Feature 3 sunny family rainy Cosine similarity
- 13. UMAP – June 2013, Rome, Italy Similarity calculation between conditions: an example in a tourism recommender 13 Similarities calculated from a tourism rating data set (Top-5 similar conditions to cold weather)
- 14. We experimented with two pairwise similarity strategies for estimating a global similarity between two situations Best-pairs All-pairs Similarity calculation between situations defined by several conditions UMAP – June 2013, Rome, Italy 14 target sunny sad unknown candidate unknown happy weekend target sunny sad unknown candidate unknown happy weekend Contextual Factors Weather Mood Day of the week
- 15. Building the semantically-related local prediction models UMAP – June 2013, Rome, Italy 15 Ratings filtering All ratings (Y) Relevant ratings (X) Local prediction model building target contextual situation (c*) Rating predictions β FOR EACH (user u, item i) in Y DO IF exists THEN ADD to X ELSE GET Sui = { : Sim(c,c*) ≥ } ADD AVG(Sui) = to X END IF END FOR
- 16. The similarity threshold controls the level of contextualization UMAP – June 2013, Rome, Italy 16 If (β = 1) works as in exact pre-filtering Sim(c,c*) = 1 only when c = c* If (β = -1) works as in the global model All ratings are used for training the model Two methods to set the optimal threshold (β) Using a global threshold for all the possible target situations Using a local threshold per target situation
- 17. UMAP – June 2013, Rome, Italy Outline 17 State of the art Novel contextual pre-filtering approach Evaluation Context-Aware Recommender Systems (CARS)
- 18. Semantic pre-filtering vs. Context-Aware MF (CAMF) CAMF-CC and CAMF-CI variants (Baltrunas et al., 2011) Two approaches as baseline Exact pre-filtering Bias MF (Context-free) Exact and Semantic pre-filtering use the Bias MF to build the local rating prediction models Experimental Setup (I) UMAP – June 2013, Rome, Italy 18 Overall rating average User bias Item bias
- 19. Rating prediction accuracy measured in terms of: Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) Per-user training/test splitting protocol For each user 5 test ratings randomly selected Only users with more than 10 ratings used for testing We discarded a test rating if the user has not rated at least 1 additional item in the same contextual situation A necessary condition for applying MF with exact pre-filtering Experimental Setup (II) UMAP – June 2013, Rome, Italy 19
- 20. 5 contextually tagged rating data sets: Different types of contextual information: One condition per situation in Music and Tourism Several conditions per situation in the other data sets In MovieLens and LibraryThing user tags are used as context Data sets UMAP – June 2013, Rome, Italy 20
- 21. Error reduction (MAE) with respect to Bias MF Performance comparison for data sets with one condition per situation UMAP – June 2013, Rome, Italy 21
- 22. Error reduction (MAE) with respect to Bias MF Performance comparison in data sets with several conditions per situation UMAP – June 2013, Rome, Italy 22
- 23. Novel contextual pre-filtering approach which is able to outperform state-of-the-art context-aware MF models by aggregating rating data acquired in situations that are similar to the target contextual situation Novel notion of semantic similarity between conditions which estimates two conditions as similar if they influence the users’ ratings similarly Conclusions UMAP – June 2013, Rome, Italy 23
- 24. New semantic pre-filtering variants To improve the precision of situation-to-situation similarities To better exploit the similarities during ratings’ filtering Extended evaluation New data sets / offline metrics (e.g. top-n recommendation) User study New semantically-enhanced context-aware approaches Using the post-filtering strategy Using the contextual modeling strategy (e.g. extended MF) Future work UMAP – June 2013, Rome, Italy 24
- 25. UMAP – June 2013, Rome, Italy 25 Exploiting the Semantic Similarity of Contextual Situations for Pre-Filtering Recommendation Any comments or questions? Victor Codina vcodina@lsi.upc.edu
Notas del editor
- Introduce myself. This work has been done with the collaboration of Francesco Ricci from the free university of bolzano and of Luigi Ceccaroni from Bdigital a technology centre in Barcelona Main message: what we present in this work?
- Divided into two parts: 1 part about main ideas behind CARS and limitations of current approaches 2 part about our novel prefiltering approach and its preliminary evaluation
- Main assumption (ratings can be influence by the current context) Explain examples (different weather conditions completely change the user experience of visiting these two points of interest)
- How do we see CARS? -> as locally adapted prediction models Main goal: to find the optimal level of contextualization. But determining this optimal level is a difficult task: several factors are influencing (the relevance of the situation, the quantity of ratings acquired in this context…)
- Is the approach that rigidly implements this idea of locally adapted prediction models Main limitation its lack of flexibility… and therefore it only works where in the target context…
- Solution based on the exploitation of hierarchical relations between conditions Main limilation is that its performance depends on the pre-defined context taxonomy which may suit or not the data. Here you can see an example of such a taxonomy in an e-commerce application.
- A More recent family of CARS approaches consists of extending the well-known MF prediction model with additional parameters… so using these approachs a global model is built The are two main ways to extending MF: TF and using contextual baseline predictors. Main limitations: Computation complexity Similarities between situations/conditions are not exploited,
- Similarly as …, we also propose a pre-filtering approach that… Key difference is the that we used a novel notion of semantic similarity based on … instead of relying on pre-defined context taxonomies. Main advantages
- Main assumption of our method for semantic similarity calculation is that The method consists of buidling a co-occurrence matrix where each entry measures the influence of the condition with respect to an item or user, than can be positive or negative. This table shows an example of such a coocurrence matrix using items of systems, in this case, points of interest.. To simplify here we only show the sign of the influence but we actually store real values. As it is shown in this formula we measure the influence… Future work: Instead of measuring the influence of each condition separately, measure it using situations directly (only most popular ones?) (maybe in tag data sets can be useful) Ricci comment: explain better the formula… (5 min extra)
- In order to mitigate the sparsity problem of high dimensional matrices… Cosine similarity of the latent feature vectors
- As an example, here I show some of the similarities estimated… In particular only shows the top-5 similar condition to the weather condition using the item-based or user-based semantics. Clear differences: item-based similarities reflect that the condtions influence similarly the same items, and in the user-based that the conditions influence similarly the same users. Unexpected similarities
- In complex situations, defined by several conditions, we need a similarity measure that can compare them. We experimented with two paiwise simialrity strategies, which compute a global similarity… Best-pairs = only best-matching pairs of conditions, as it is shows in the example… All-pairs = all the possible similarities
- Having these similarities between situations, our prefilering approach works as follows: given… and given… we select as relevant ratings those that were acquired.. After this process, in the case of having several ratings for the same user and item we … Once selected the relevant ratings we use them to …
- Threshold controls the level of contextualization (1 exact prefiltering and -1 global model) It can be learnt at different granularities. Two variants: one using a global threshold …
- Performance comparitive with respect to state of the art MF model for building local models in prefiltering approaches in order to properly compare the perfomrance (but other recommendation models can also be used) In particular we have used a standard MF…
- Rating prediction accuracy metrics Per-user training/test splitting protocol In this experiment we only used as test ratings those provided in contextual situations in which the user rated at least 1 additional item, because it a necessary condition of exact pre-filtering to be able to make predictions.
- We have considered 5 data sets
- Bar charts show performance using data sets where… here we compare two variants: one using the global threshold…. On the one hand, semantic prefiltering variants outperform On the other hand, the one using the global thresholds achieves the best results in both data sets (change color)