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- 1. Personalized review recommendation Part 2 M.Chelliah and Sudeshna Sarkar 1
- 2. Deep Learning in Review Recommendation 2
- 3. Feed forward network β An artificial neuron β Receives inputs β Summation followed by a nonlinear activation. β Produces an output for next layer neurons β Weights learnt through back propagation Source:https://en.wikipedia.org/wiki/Connectionism
- 4. MultiLayer Perceptron xn x2 x1 am a1 o output x1 x2 xn Hidden neuron z1 a1= g(z1) w11 w12 w13 x W z|a sigmoid tanh ReLU gradient ~ 0
- 5. xn x2 x1 x W[1] z[1]|a[1] W[2] output oK o2 o1 softmax MultiLayer Perceptron - loss functions Learn weights by minimizing the loss
- 6. DL in Recommendation Systems β Deep learning based models achieved the best performances and is a promising tool for recommender problems β Representation plays an important role in Recommender Systems. Multiple sources of data may be leveraged for rich representation β Representation of users and items β Transactions β Content: Product description, Metadata, Reviews, Product Image β User demography β Product ontology 6
- 7. Using all embeddings β’ Multiple ways of learning/combining representations β’ Example 1: (Adapted from one experiment at Flipkart Recommender System) β’ Concatenate different pre-trained embeddings into a single vector β’ Learn a simple Logistic Regression OR β’ Feed into another ANN with softmax output (corresponding to purchase or clicks) β’ Example 2: Joint learning of the embeddings β’ Similar products - Locality Sensitive Hashing (LSH) on embeddings CF embedding Image embedding Text embedding Hidden layer Output layer
- 8. Representation of Items, Users, Reviews Representation of items and/or users is necessary for getting a recommender system to work. β’ One-hot representation β’ Embedding as vectors: pre- trained β’ Embedding can be learned on the task β’ Item Representation 1. Based on item interaction sequences and item properties β Prod2vec, Meta Prod2vec etc 2. Based on user-item interaction β’ Neural collaborative filtering: Model user-item interactions 3. User and item information (content) β’ Review representation β’ User representation
- 9. Product Embedding Prod2vec or Item2vec : Product embedding β’ Based on item-item co-occurrence from transaction sequences co-purhased products) β’ Uses method of word embedding: low-dimensional, distributed embeddings of words based on word sequences in text documents 1. Barkan, Oren, and Noam Koenigstein. "Item2vec: neural item embedding for collaborative filtering." Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on. IEEE, 2016. 2. Grbovic, Mihajlo, et al. "E-commerce in your inbox: Product recommendations at scale." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015.
- 10. β’ Representation of words ``Similar words have similar contextsβ 1. CBOW: P Word Context 2. Skipgram: P(Context|Word) Word2vec Input Projection Output Input Projection Output
- 11. Skipgram Model β’ Input: Central word π€π€π‘π‘ β’ Output: Words in its context: ππππππππ π€π€π‘π‘βππ, β¦ , π€π€π‘π‘β1 , π€π€π‘π‘+1, β¦ , π€π€π‘π‘+ππ β’ Each input word represented by a 1-hot encoding of size V Source Text: Deep Learning attempts to learn multiple levels of representation from data. Input output pairs : Positive samples: β’ (representation, levels) β’ (representation, of) β’ (representation, from) β’ (representation, data) Negative samples: β’ (representation, x) [x: all other words except the 4 positive] π€π€π‘π‘ π€π€π‘π‘+1 π€π€π‘π‘+ππ π€π€π‘π‘βππ π€π€π‘π‘β1 INPUT PROJECTION OUTPUT
- 12. Prod2vec Use word2vec on co-purchased products Purchase sequence of user u πππ’π’π’, πππ’π’π’, β¦ , πππ’π’π’π’ Skipgram applied on transaction sequence πππ‘π‘ πππ‘π‘+1 πππ‘π‘+ππ πππ‘π‘βππ πππ‘π‘β1 Input Projection Output Positive samples: Negative samples πππ’π’,π‘π‘, πππ’π’,π‘π‘βππ πππ’π’,π‘π‘, πππ’π’,π‘π‘β1 πππ’π’,π‘π‘, πππ’π’,π‘π‘+1 πππ’π’,π‘π‘, πππ’π’,π‘π‘+ππ ππππ, ππππππππ πππ’π’,π‘π‘, p p is any product other than ππππππππ
- 13. Extensions of prod2vec 1. Meta prod2vec β’ Use product metadata in addition to transaction sequence β Category, Brand, Description, etc β Embedded metadata added both to input and context 2. Several other *2vec models πππ‘π‘ πππ‘π‘+1 πππ‘π‘+1 πππ‘π‘β1 Input Projection Output πππ‘π‘ πππ‘π‘β1 ππππ ππππ
- 14. Review Representation β Word2vec for word representation (concept representation) β Document or paragraph representation β Averaging or summing of word vectors β Paragraph or document vector using CNN, RNN, etc 17
- 15. Convolution for Text this sequence of word vectors represents a text ππ Γ ππ representation of sentence ππ1 ππ2 Filter of size 2 Γ ππ Feature map of 1 filter π₯π₯1 π₯π₯2 π₯π₯ππ π₯π₯3 ππππ = ππ π€π€ β π₯π₯ππ:ππ+ββ1 + ππ
- 16. CNN for Text this sequence of word vectors represents a text ππ Γ ππ representation of sentence Feature maps of ππ filters β¦β¦.β¦ π₯π₯1 π₯π₯2 π₯π₯ππ π₯π₯3
- 17. CNN for Text: Kimβs CNN Kim, Yoon. "Convolutional neural networks for sentence classification." arXiv preprint arXiv:1408.5882 (2014).
- 18. Kimβs CNN β Word embedding β Padding to size n Dropout Rescale the weight vectors of each class to fixed number s 1. Convolution layer 2. Max-pooling layer ππΜ = max ππ 3. Fully connected layer with softmax output
- 19. Dynamic k-max pooling Handles sentences of varying length k is a function of the sentence length and network depth ππππ = max πππ‘π‘π‘π‘π‘π‘, πΏπΏ β ππ πΏπΏ β π π πποΌcurrent convolution layer πΏπΏοΌNumber of convolutional layers π π : Sentence length πππ‘π‘π‘π‘π‘π‘οΌfixed pooling parameter for the topmost convolutional layer Neural Network for Modeling Sentencesβ by Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom arXiv, 2014
- 20. CNN to find Document Latent Vector β’ CNN Architecture to generate document latent vector. 23 Item Text Review, etc. Convolution Maxpooling . . .. . .. . .. . . . . .. . .. . .. . . . . .. . .. . .. . . . . .. . .. . .. . . β ππππβ¦ Output layer Projection Document Latent Vector
- 21. Recurrent Neural Networks: make use of sequential information 24 Input at time step π‘π‘ β 1 Hidden state at time step t Output state at time step t Activation function
- 23. Long term dependencies β In practice, RNNs cannot capture long term dependencies. 26
- 24. RNN and LSTM 27
- 25. Long Short Term Memory (LSTMs) 28 β Provide highway to pass cell state (or memory). β LSTM can add or remove information to the cell state through regulated structures called gates. LSTM has three gates to protect and control cell state
- 26. f u LSTMs β Details Forget Update + tanh output tanh softmax o Ct-1 ht-1 xt Ct ht C~ t yt
- 27. Forget gate β Forget gate layer decides what information will be thrown away β Looks at βπ‘π‘β1 and π₯π₯π‘π‘ and outputs a number between 0 and 1 β 1 represents completely keep this, 0 represents completely get rid of this β Example: forget the gender of the old subject, when we see a new subject 30
- 28. Update β A sigmoid layer (input gate layer): decides what values weβll update β A tanh layer: creates a vector of new candidate values, πΆπΆπ‘π‘ οΏ½ 31 Input gate layer tanh layer
- 29. Update 32 β’ Update old state by πΆπΆπ‘π‘β1 into the new cell state πΆπΆπ‘π‘ β’ Multiply old state by πππ‘π‘ β’ Forgetting the things we decided to forget earlier β’ Then we add πππ‘π‘ β πΆπΆπ‘π‘ οΏ½
- 30. Output β A sigmoid layer decides what parts of the cell state to output β Put the cell state through tanh and multiply it by the output of the sigmoid gate 33
- 31. Gated Recurrent Unit β Replace forget (f) and input (i) gates with an update gate (z) β Introduce a reset gate (r ) that modifies ht-1 β Eliminate internal memory ct Source: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
- 32. Bidirectional LSTM w1 w2 w3 wn
- 33. Attention Mechanism β Pool of source states β Retrieve as needed 36
- 34. Attention - Context 37 πππ‘π‘ = οΏ½ πππ‘π‘ π π βοΏ½π π π π βπ‘π‘ = ππ(βπ‘π‘β1, πππ‘π‘, πππ‘π‘)
- 35. Sentence Modeling β Word Average β CNN β LSTM: A sentence π π is transformed to a fix-length vector πππ π by recursively applying a LSTM unit to each word embedding πππ€π€π‘π‘ and the previous step βπ‘π‘β1. β BiLSTM: Bi-directional LSTM (BiLSTM) captures both the left and right context. β Two hidden states βπ‘π‘ ππππππππππππππ and βπ‘π‘ πππππππππ€π€π€π€π€π€π€π€ are concatenated into a final hidden state βπ‘π‘ ππππππππππππ 38
- 36. Joint deep modelling of Users and Items using Review for Recommendation β A shared layer helps couple latent factors learned for user behavior and item properties from reviews by two parallel networks 39 Joint deep modelling of Users and Items using Review for Recommendation, Lei Zheng, Vahid Noroozi, Philip S. Yu, WSDM 2017
- 37. DeepCONN Architecture 40 User Review Text Item Review Text Factorization Machine TCNNu TCNNi π₯π₯π’π’ π¦π¦ππ User Network Item Network
- 38. 41 Li P, Wang Z, Ren Z, Bing L, Lam W. Neural rating regression with abstractive tips generation for recommendation, SIGIR 2017. Abstractive Tips Generation β Gated recurrent neural networks help translate user and item latent representations into concise abstractive tips with good linguistic quality simulating user experience and feelings β Deep Composite Model : MLP and RNN β Two Tasks 1. Rating prediction with MLP 2. Tips Generation
- 39. 42
- 40. Neural Rating Tips β Examples of the predicted ratings and the generated tips. β The first line of each group shows the generated rating and tips. β The second line shows the ground truth. 43
- 41. Unified Representation By translating various sources (e.g., review, rating) into a unified representation space, heterogeneous information can be integrated for informed recommendation. 44 Zhang Y, Ai Q, Chen X, Croft WB. Joint representation learning for top-n recommendation with heterogeneous information sources, CIKM 2017.
- 42. NAPRE: Neural Attentional Rating Regression with Review-level Explanations β To predict a rating given a user and an item, as well as to select both useful and representative reviews. β Useful reviews are obtained through a attention mechanism and provide explanations for users to make better and faster decisions. β NAPRE learns the usefulness of each review. 45 Chen C, Zhang M, Liu Y, Ma S. Neural Attentional Rating Regression with Review-level Explanations, WWW 2018.
- 43. TextCNN β CNN text Processor: inputs a sequence of words and outputs a n-dimensional vector representation for the input. 46
- 44. NAPRE β Utilize the attention mechanism to assign weights to reviews when modeling users and items. β Two parallel neural networks for user modeling and item modeling. β A prediction layer to let the hidden latent factors of user and item interact. β The training data consists of users, items, and text reviews. β Test stage: only users and items are available. 47
- 45. Item Modelling β CNN Text Processor is applied to process the textual reviews of item i. β Each review of i is transformed into a matrix of word vectors, (Vi1, Vi2,...Vik). β These matrixes are sent to the convolutional layer and the feature vectors of them can be obtained from the output as (Oi1, Oi2, ...Oi j) . β Attention mechanism is used to learn the weight of each review. 48
- 46. Attention-based Review Pooling β Select reviews that are representative to item iβs features β Aggregate the representation of informative reviews to characterize item i. β A two-layer network is applied to compute the attention score ππππππ . β The input contains the feature vector of the lth review of item i (ππππππ) and the user who wrote it β (ID embedding, π’π’ππππ ) models the quality of users, helps identify users who always write less-useful reviews. 49
- 47. Attention-based Review Pooling β The attention network: ππβ ππππ = βππ ππππππππ ππππ ππππππ + πππ’π’ π’π’ππππ + ππ1 + ππ2 β Normalize: ππππππ = exp(ππβ ππππ) β exp(ππβ ππππ)ππ ππ=0 β the feature vector of item i: ππππ = β ππππππ ππππππππ=1β¦ππ β The output of the attention-based pooling layer is a k1 dimensional vector β A fully connected layer computes the final representation of item i: ππππ = ππ0 ππππ + ππ0 50
- 48. NAPRE: Prediction Layer β NAPRE extends user preferences and item features in LFM model to two components: one based on ratings while the other based on reviews. β A neural form LFM used for predicting ratings. β the latent factors of user and item are mapped to a shared hidden space. β The interaction between u and i is modelled as: β0 = (πππ’π’ + πππ’π’)β¨(ππππ + ππππ) 51
- 49. NAPRE: Prediction Layer β The interaction between u and i is modelled as: β0 = (πππ’π’ + πππ’π’)β¨(ππππ + ππππ) β πππ’π’ and ππππ are user preferences and item features based on ratings from LFM β πππ’π’ and ππππ are user preferences and item features obtained from the above method β β¨ denotes the element-wise product of vectors. β The output is a n-d vector, passed to prediction layer: π π οΏ½π’π’,ππ = π€π€1 ππ β0 + πππ’π’ + ππππ + ππ 52
- 51. Aspect Similarity Recognition using Deep Learning β Two sentences are identified as aspect similar if they mention at least one aspect in common 55 A Deep Learning Study of Aspect Similarity Recognition Huy-Tien Nguyen, Quan-Hoang Vo, and Minh- Le Nguyen, 2018
- 52. Recommendation of High Quality Representative Reviews in E-commerce Recsys 2017, Debanjan Paul, Sudeshna Sarkar (IIT Kharagpur) Muthusamy Chelliah, Chetan Kalyan, Prajit Nadkarni (Flipkart)
- 53. Product Reviews Aspect and Sentiment Extraction Review Selection Preserves Statistical Distribution Subset of βkβ Reviews Aspect Synonymy Detection Review Quality Estimation Set of annotated review data Distribution of aspects and sentiments Product aspects and sentiments Review quality score System Architecture
- 54. Product Reviews Aspect and Sentiment Extraction Review Selection Preserves Statistical Distribution Subset of βkβ Review s Aspect Synonymy Detection Review Quality Estimation Set of annotated review data Distribution of aspects and sentiments Product aspects and sentiments Review quality score Extracts product aspect phrases along with the corresponding sentiments from each reviews of the item. E.g. The lens of this camera is great. Considers semantic similarity between aspects to merge similar aspects. E.g. βPhotoβ, βPictureβ, βcameraβ refer to the same feature of a mobile phone. Estimates review quality score from its textual content Representative Reviews Covers different product aspects and sentiments. Preserves the statistical distribution of product aspects and opinions.
- 55. Aspect Extraction Extracts product aspect phrases from each reviews of the item along with the sentiments expressed about them. Sample Input: The picture quality is great. It also has cool looks. But the phone hangs sometimes, if you install too many apps. Output: picture quality,+1 ; looks,+1 ; processor,-1 Qiu, Guang, et al. "Opinion word expansion and target extraction through double propagation." Computational linguistics 37.1 (2011) Use a semi-supervised technique based on bootstrapping from a small opinion lexicon. Utilize several syntactic relations that link opinion words and targets to expand the initial opinion lexicon and to extract targets.
- 56. Evaluation of Aspect and Sentiment Extraction Module β’ Used 300 reviews from mobile phone, camera and blue tooth headsets product in Amazon dataset β’ Manually annotated to find the correct aspect phrases and corresponding sentiment No. of reviews 300 Actual no of aspect phrases 660 No of extracted aspect phrases 630 Correctly extracted aspect phrases 468 Precision 70.90%
- 57. Aspect Synonymy Detection General Display Processor Memory Camera Connectivity Multimedia Charging 56% 47% 16% 6% 29% 49% 34% 36% Audio quality Video Recording Sound quality Photo Camera Bluetooth headset RAM Picture quality Touch Screen Battery life β¦ 2% 0% 1% 1% 10% 6% 0% 2% 1% 7% β’ 125 reviews of an βAsus Zenfone Maxβ. β’ 35 aspects before: audio quality, video recording, sound, photo, camera, bluetooth headset, RAM, picture quality, touch screen, battery life, picture, clock speed, frequency, OS, SIM, packaging, network, model, ROM, pixel, LED, button, power, price, formats, display, disk, processing, configuration, thing, smartphone, support, resolution. Use word embeddings of words and phrases. Merge phrases with similar word vectors. 8 aspects obtained.
- 58. Evaluation of Aspect Similarity Detection Module β’ We extracted aspect phrases for 10 products in mobile phone domain and mapped the extracted aspect phrases with the Flipkart catalogue aspects. β’ We have manually evaluated the correctness of the mappings obtained. β’ The accuracy of our aspect similarity detection module is 83.75%. Aspect Category #Extracted aspect phrases Correctly mapped Incorrectly mapped Camera 360 342 18 Connectivity 135 63 72 Dimension 99 45 54 Display 306 198 108 General 423 333 90 Memory 27 27 0 Multimedia 45 27 18 OS 207 207 0 Others 1062 963 99 Not Mapped 216 207 9 Total 2880 2412 468
- 59. Review Quality Estimation β’ Some review recommendation systems use review helpfulness score as the parameter to judge review quality. β’ New reviews do not have any helpfulness score (cold start). β’ Not all sites capture helpfulness score. β’ We develop a CNN (convolutional neural network) to estimate the quality score of the textual reviews. β Simple CNN β Dynamic CNN with k-max pooling
- 60. Dynamic Convolutional Neural Network Architecture Input Layer β’ All reviews of a product (length s) β’ Each word is embedded as a d-dim vector. Convolutional Layer β’ A convolution matrix is applied to input matrix S (filter size of m) Dynamic K-max pooling layer β’ Selects K most active features. β’ K is determined dynamically as a function of the length of the sentence and depth of the network . πΎπΎππ = max πΎπΎπ‘π‘π‘π‘π‘π‘, πΏπΏ β ππ β π π πΏπΏοΏ½
- 61. Dynamic Convolutional Neural Network Architecture Folding β’ Elements in two rows of the matrix are added component wise in order to obtain a reduced matrix of dimension d/2. Fully Connected Layer β’ Fully connected softmax layer.
- 62. Experiments β’ Amazon dataset. β’ Reviews from six different product domains β electronics, clothing, books, shoes, music, watches β’ Accuracy: β CNN: 71% β DCNN: 83% Number of reviews 34,686,770 Number of users 6,643,669 Number of products 2,441,053 Users with > 50 reviews 56,772 Median number of words per review 82 Timespan Jun 1995 β Mar 2013
- 63. Review Selection β’ Given a corpus of reviews R on an item, find a k-size subset S β R of reviews that accurately capture the proportion of opinions of the itemβs features. β’ For t = 1 to k 1. Pick one review r Ο΅ R to form subset S(t) β R 2. so that the distance D( Ο( S(tβ1) βͺ {r}), Ο) is minimized, where π π is the percentage distribution of the aspects in the set π π (S) and D is the L2 2 norm of their difference i.e. π«π« π π , ππ = π³π³ππ 2 π π β ππ = β π π ππ β ππ ππ ππππ ππ=ππ , where m is the total number of extracted aspects. Lappas, Theodoros, Mark Crovella, and Evimaria Terzi. "Selecting a characteristic set of reviews." Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012
- 64. Sample Output β’ Product Name: Plantronics Voyager 510 Bluetooth Headset β’ Number of Reviews : 465 β’ Number of Attributes (Catalogue Attributes) : 8 β’ Original attribute distribution: General Display Processor Memory & Storage Camera Connectivity Multimedia Charging 60% 60% 20% 0% 20% 40% 40% 40% General Display Processor Memory & Storage Camera Connectivity Multimedia Charging 56% 47% 16% 6% 29% 49% 34% 36% Resulting attribute distribution in recommended reviews:
- 65. System Evaluation We have recommended three sets of reviews for 8 products based on: β’ RQS: Explicit review helpfulness score. β’ CNN: Quality score of review estimated by CNN β’ DCNN: Quality score of review estimated by DCNN β’ CRS: Characteristic Review Selection algorithm. β’ COMB: Combined approach β’ 3 sets of 5 reviews from the methods are given to 10 human annotators to rank the methods from 1 to 3. β’ Ranking done on basis of which set of reviews defines the product best
- 66. Comparison of RQS,CNN and DCNN method β’ DCNN has an accuracy of 83% wrt RQS β’ Plain CNN has 70%. β’ COMB is declared as the best method in 60% of the reports obtained from annotators. β’ COMB approach has the lowest rank. Method Percentage Average Rank RQS 35 1.925 CNN 20 2.2625 DCNN 45 1.7875 Method Percentage Average Rank CRS 15 2.175 CNN 25 2.2125 COMB 60 1.6125
- 67. Live Evaluation of the system at Flipkart Live Evaluation of COMB method against default recommendation system at Flipkart β’ 100 sorted reviews recommended by COMB method for 24 random products in mobile phone category. β’ 25% of users experience the reviews recommended by our method while rest experience the default behavior. β’ Impact is measured in number of transactions (Txns) per product page visit (PPV). 0 0.5 1 1.5 2 Product1 Product3 Product5 Product7 Product9 Product11 Product13 Product15 Product17 Product19 Product21 Product23 Default COMB β’ COMB shows an increase in product purchase for 10 products. β’ Default recommendation system performs better in case of 5 products. β’ Similar performance in case of 9 products.
- 68. Live Evaluation of the system at Flipkart β’ Net product transaction increase by 1.85% out of 1 million product units by using COMB. β’ COMB combines quality score of reviews with the product aspects and sentiments along with maintaining statistical distribution of opinions found in underlying corpus. β’ COMB has performed better than both CNN and default recommendation system at Flipkart live testing. Algorithm Txns PPV Default 1777 313776 COMB 1781 308782 Increase in conversion by 1.85%
- 69. List of reviews recommended by CRS method Current List of reviews recommended by COMB I love my headset. It is clear and comfortable. I could wear it all day. Works well, good audio quality, battery lasts a long time. I bought this model for my wife after her last one died. It was her third or fourth..? I've lost track. This model does not compare to the others; it's much better. It hooked up easy to her Verizon phone and sounds clear on BOTH ends. I hear her fine and her voice is crisp and clear. What else is there? Oh yes, price.... around $40...Buy one, you will not be sorry. I have been using this headset for about a month. The sound quality is better than most headsets that I have used. One day when it was very windy there was a little too much wind noise in the mic for the person that I was speaking to. The device automatically connected to my RAVR phone so connection was not an issue. I am not crazy about the connect button on the mic arm, and open the phone to connect to calls The set comes with three different sizes of ear pieces which makes it fit well for almost everyone. The battery life on this device is amazing Doesn't work. Will be sending back---home. Tried pairing it up over 10 times with Iphone, which is with Verizon, and it did not work with phone. Expecting a refund back when I get the CD back to send with the rest of items. Thank you anyway. I bought the 510 based on the volume of positive reviews. Well if this truly is the best Bluetooth headset then I'm going back to using a wired headset. Sound quality was poor, people couldn't hear me if there was any background noise and the ear pieces were all uncomfortable.
- 70. Personalised review recommendation in e-commerce 74 Mtech thesis of Surjodoy Ghosh Dastider, IIT Kharagpur, 2018 Review 1: Good UI, user-friendly, sturdy Review 2: Poor battery life and performance of apps ... User 1: Prefers user- friendly phones User 2: Prefers high- performance phones Product: Mobile Phone XYZ
- 71. Methodology Our proposed review recommendation system performs the following tasks: 1. Extract aspects and corresponding sentiments from all reviews and questions in a given product domain. 2. Represent each review, product and user as a probability distribution over topics. 3. Generate helpfulness/quality score for each review. 4. Generate the desired aspect distribution in the recommended set of reviews, using product and user profiles. 5. Select an optimal subset of reviews to recommend to a user. 6. Use user cluster profiles instead of individual user profiles. 75
- 73. Domain Topic Identification β We use a topic model, Sentiment-LDA (Li β10) for aspect based sentiment analysis (ABSA), to extract product aspects and corresponding sentiments. β Given a product domain, we discover the optimal number of global topics T for all product reviews in that domain. β For each topic-sentiment pair, we retrieve the top 30 words 77
- 75. Representation of Reviews and Products We analyze each review r to get a vector V(r) of size 2*T. Similarly, we find vectors from questions available in the e-commerce platform. For each product P, we aggregate vectors of all reviews Rev(P) written for that product to generate the product vector V(P). V (P) = Ξ£rβRev(P) V (r) 79
- 76. Representation of Users For each user U, we used three sources of information: β Reviews written by that user Revu(u) UR(u) = Ξ£r β Revu(u)V (r) β Questions asked by that user Ques(u) UQ(u) = Ξ£q β Ques(u)V (q) β Reviews up-voted/down-voted by that user Vote(u) UV(u) = Ξ£r β Vote(u) V (r) Each such user vector gives the distribution of topics over each user, or the aspects that each user is interested in. Some or all of them can be used to build the user profile U(u). 80
- 77. Personalization Strategy The base review recommendation model considers the product vector V(P) as the desired distribution of topics with sentiments in the recommended review set. A personalized system needs to take the user interests into consideration. For this we use the user profile U(u). We felt that it is important to balance the inputs from U(u) as well as V(P) to form the desired topic distribution in the recommended review set. Thus the desired distribution is given by D(p, u) = f(V(p), U(u)). 81
- 78. Personalization Strategy (contd.) We considered the following forms for the composition: D(p, u) = V(p)Ξ± . U(u)Ξ² 1. V(p): Ξ± = 1, Ξ² = 0. 2. U(u): Ξ± = 0, Ξ² = 1. 3. A combination of both the terms, obtained by keeping Ξ± = 1 and varying Ξ². A higher value of Ξ² corresponds to more aggressive personalization. 82
- 80. User Clustering Drawback: Latency issues when used in a online e-commerce platform. The reviews to be recommended to a user may be pre-computed in order to serve users in real time. Workaround: Group users based on the similarity of their user profiles so that different review recommendations may be associated with each group. Used a clustering algorithm (k-means) to group the users based on their aspect preferences for each domain and created c groups of users. Computed the mean vector U(Ci) of each group, and replaced the user's own vector U(u) with the mean vector U(Ci) of the user's group. 84
- 81. Dataset Mobile Phones Number of Reviews: 100000 Number of Products: 5987 Number of Users: 93434 85 Computers Number of Reviews: 40958 Number of Products: 3455 Number of Users: 39336 β Used reviews from the product review dataset of Flipkart. β Each review item includes product title, user info, review rating, and plaintext review. β Reviews chosen from two popular product domains - mobile phones and computers.
- 82. Evaluation Strategy A way to evaluate the system is to check whether the reviews recommended by the system are found to be helpful to the user. We can find whether the reviews of a product that a user actually up-voted were recommended by the review recommendation algorithm. We used the offline review up-vote data by users for this purpose. 86
- 83. Evaluation Strategy Supposing that from the review set R = Rev(p), RU is the set up-voted by the user, and S is the set recommended. We evaluate the performance of our system using average Precision and average Recall. Precision: Percentage of reviews in the recommended set S which have been up-voted. Recall: Percentage of up-voted reviews RU returned in the recommended set S. 87
- 84. Experiments 1. For aspect-sentiment extraction, 6 topics were taken for cell phones and 8 topics were taken for computers. Number of sentiments was taken as 2. 2. We used the following Dirichlet priors for our topic model: Ξ± = 0.1, Ξ² = 0.01, Ξ³ = 1. 3. Using the resulting global topics, we created vectors of size 6*2=12 for each mobile phone review, and of size 8*2=16 for each computer review. Based on this we created product profiles for each product using Rev(p), and user profiles for each user by using three sources of information: UR(u), UQ(u) and UV(u). 88
- 85. Experiments 4. We used the following different combinations of the aforementioned user vectors to obtain the final user vector U(u) for our personalization step: a. UR = UR(u) b. URQ = UR(u) + UQ(u) c. URV = UR(u) + UV(u) d. URQV = UR(u) + UQ(u) + UV(u) 5. Given a user u, a product p, its reviews Rev(p), we recommended a subset SK u,p of K reviews using our algorithm based on our desired distribution D(p, u). 89
- 86. Testing We took N=1000 products from each domain with the highest review count. For each product, we randomly selected one user who has up-voted more than three reviews of that product, thus creating N product-user pairs. For each product-user pair, we generated K recommendations. We experimented with the recommendation set size K, using values of 5, 10 and 15. 90
- 87. Results β The system's recall for both domains increases with increase in K. β We found that the user profile URQV computed using all information works best. β We next ran the grouping based recommendation and experimented for group sizes of 5, 10 and 15. We found that the results using the group profile is better than the non-personalized case even for 5 groups. In most cases the results are better with more number of groups (c). β For the mobile phone domain with K=5, the best precision was 11.57% for c=15 as compared to 12.04% for the individual approach. β The best recall obtained for K=15 is 26.76% for mobile phones and 23.85% for computers. 91
- 88. Results for mobile phone domain, K = 15, individual profiles 92
- 89. Results for computer domain, K = 15, individual profiles 93
- 90. Group Personalization Results, K = 15, D(p,u) = V(p) Β· U(u)5 94 Mobile Phones Computers
- 91. Conclusions and Future Work β Our system for personalized review recommendation using individual profiles performs better than other proposed systems addressing the same problem. β The group based implementation is novel and makes our system practical. β We plan to perform live testing and check the effectiveness of our method in increasing product purchases. 95