{"id":10707,"date":"2023-09-27T12:50:27","date_gmt":"2023-09-27T12:50:27","guid":{"rendered":"https:\/\/www.sonyresearchindia.com\/decoding-cd-hrnn-content-driven-hrnn-to-improve-session-based-recommendation-system-with-brijraj-and-sonal-copy\/"},"modified":"2023-11-30T13:10:07","modified_gmt":"2023-11-30T13:10:07","slug":"decoding-cr-sorec-bert-driven-consistency-regularization-for-social-recommendation","status":"publish","type":"post","link":"https:\/\/whiteriversmediasolutions.com\/Sony\/decoding-cr-sorec-bert-driven-consistency-regularization-for-social-recommendation\/","title":{"rendered":"Decoding \u2018CR-SoRec: BERT driven Consistency Regularization for Social Recommendation\u2019"},"content":{"rendered":"\t\t
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BLOGS<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Decoding \u2018CR-SoRec: BERT driven Consistency Regularization for Social Recommendation\u2019<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBy Tushar Prakash, Data Science Intern at Sony Research India\n\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In this blog, Tushar Prakash summarises the paper titled \u2018CR-SoRec: BERT driven Consistency Regularisation for Social Recommendation\u2019<\/a> co-authored by Raksha Jalan, Brijraj Singh, Naoyuki Onoe which was accepted at the Recommender Systems 2023 (RecSys) Conference<\/a>, hosted in Singapore between 18th-22nd September 2023.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Abstract<\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In the real world, we often ask our friends for various recommendations. With the rise of social media, we can now do the same with our online connections. Social recommendations combine social connections with user-item interactions to give better recommendations. However, current methods have two limitations: they don\u2019t fully explore the complex relationships between neighbours\u2019 influences on user preferences, and they are prone to overfitting due to limited user-item interaction records.<\/p>

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To solve these problems, we propose a new framework called CR-SoRec. This framework uses BERT and consistency regularization to learn context-aware user and item embeddings with neighbourhood sampling. It also leverages diverse perspectives to make the most of the available data. Our model aims to predict what item a user will interact with next based on their behaviour and social connections. Experimental results show that it outperforms previous work by a significant margin and defines a new state-of-the-art. We also conduct extensive experiments to analyse the proposed method.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Introduction<\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Today, many e-commerce and online platforms have doubled down as social media platforms. For instance, Amazon\u2019s \u201cWatch Party\u201d and Spotify\u2019s \u201cBlend\u201d allows users to invite friends to watch content and share playlists.<\/p>

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To improve such recommendation systems, it is important to incorporate social interactions into the model. However, high-order social relations make it challenging to extract relevant data for modelling user preferences. Our proposed framework, CR-SoRec, uses BERT and a Consistency Regularization Framework to efficiently learn user-item and user-user social representations. This is achieved by generating robust user-item interactions representation through user header with neighbourhood sampling. The proposed method also helps to eliminate insignificant signal from user-item interaction history. Data augmentation is performed to improve data diversity and the model\u2019s robustness. The proposed network is trained by minimizing a combination of three types of losses. The main contributions of the paper include proposing a novel way to learn User\/Item representations based on neighbourhood sampling in conjunction with BERT, designing two novel Consistency Regularization (CR) tasks- Item CR and Social CR, and proposing a new way to utilize social connection and user-item interactions with CR to enhance social recommendation performance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Method<\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The proposed method consists of following components:<\/h6>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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1 Embedding generation layer:<\/h6>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Studies on social recommendation have shown a positive correlation between user social behaviour and item interactions. To capture this correlation, we propose enriching user and item embeddings with influential neighbours through neighbourhood sampling.<\/p>


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In the recommendation task, we are given a sequence of items that a user has interacted with. To generate a training sample, we randomly mask some items in the sequence using the classical Cloze task. The entire process of embedding generation with neighbourhood sampling is presented in Algorithm 1. <\/span><\/p>\n

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We perform neighbourhood sampling using a multinomial distribution, which incorporates information from the most influential neighbours. To bring similar user-item pairs closer in the embedding space, we introduce a user header that generates the user embedding E(u). In Algorithm 1, item-user interaction history (H\ud835\udc63\ud835\udc62) consists of a list of all users who have interacted with item \ud835\udc63\ud835\udc56 in the past, while item-item similarity (S\ud835\udc63\ud835\udc63) represents similar items that have more than 50% of common users.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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2 BERT Network:<\/h6>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe BERT model is a powerful deep learning model that utilizes multiple bidirectional transformer layers and self-attention mechanisms. It is designed to learn a deep bidirectional representation of input sequences for a particular task. In our case tasks are recommendation, social consistency regularization, and item consistency regularization. The embedding of these sequences is generated by passing them through an Embedding Generation layer, after which the generated embeddings are passed to a shared BERT network. This network consists of transformer layers containing Multi-Head Self-Attention and Position-wise Feed-Forward Network. The Multi-Head Self-Attention layer captures long-range dependencies between representation pairs in the sequences by projecting hidden representations into multiple subspaces and applying multiple attention functions in parallel. The output representations are then passed through a Position-wise Feed-Forward Network. For the recommendation task, a Linear Classification layer is also used to predict the next interacted item.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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3 Consistency Tasks:<\/h6>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTo improve the model\u2019s performance and prevent over-fitting, we used Consistency regularization (CR) on item interactions and the user\u2019s social network. We also generated diverse data by creating different views of the original sequence through augmentation. With CR, the model predicts both the original and augmented input sequences, ensuring consistency between the two through a penalty term in the loss function. Both Item and Social Consistency Regularization help with the recommendation task.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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1 Social Consistency Task:<\/h6>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIn this task, two versions of embeddings are generated by passing through the embedding generation layer. The first version uses a masked item input sequence and the user\u2019s social network, while the second version uses a masked input sequence and an augmented user social network. Both versions of embeddings are then passed to BERT to generate two different representations. To ensure that both representations are similar, an L1 penalty loss is applied as described in Algorithm 3.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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2 Item Consistency Task:<\/span><\/h6>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This task involves passing the original input sequence and its augmented version through BERT, after generating embeddings from the embedding layer. BERT provides distinct representations for both versions of the input sequence. Finally, a penalty loss is employed to minimize their L1 distance, thus enforcing similarity between the two representations as described in Algorithm 2.<\/p>\n\n

Note: The input sequence used in this task is not masked.<\/span><\/div>\n
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<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\nThe proposed model is trained by jointly minimizing the cross-entropy loss for the recommendation task, and two different L1 losses for both consistency tasks. The figure 1 illustrates the overall workflow of the proposed model.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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2 Item Consistency Task:\n<\/h6>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This task involves passing the original input sequence and its augmented version through BERT, after generating embeddings from the embedding layer. BERT provides distinct representations for both versions of the input sequence. Finally, a penalty loss is employed to minimize their L1 distance, thus enforcing similarity between the two representations as described in Algorithm 2.<\/p>

Note: The input sequence used in this task is not masked.<\/p>

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The proposed model is trained by jointly minimizing the cross-entropy loss for the recommendation task, and two different L1 losses for both consistency tasks. The figure 1 illustrates the overall workflow of the proposed model.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Experiments Details<\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWe evaluate the proposed model using the Epinion, Ciao, and Yelp datasets, splitting them into 80% for training, 10% for validation, and 10% for testing. To evaluate the recommendation performance, we use the standard evaluation metrics of Normalized Discounted Cumulative Gain (NDCG) and Hit Ratio (HR). We tested three different augmentation techniques for consistency tasks: Reordering, Masking, and Cropping. Among these techniques, Cropping performed the best.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Performance evaluation<\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTable 1 displays the comprehensive results of our experimentation. Notably, our innovative CR-SoRec model has exhibited substantial performance gains over existing benchmarks across all datasets. This improvement is particularly evident when compared to the attention-based DICER model and graph-based approaches like Diffnet, Diffnet++, and ConsisRec. This underscores the criticality of incorporating bidirectional context from user-item interactions in representation learning.\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe remarkable strides made by CR-SoRec in contrast to prevailing deep learning-based Social Recommendation models underscore the potency of Consistency Regularization when coupled with our novel embedding layer and BERT. This amalgamation effectively captures the evolving interests of users by offering a more robust representation of their interaction history. Furthermore, to underscore BERT\u2019s significance in our context, we conducted an experiment wherein we replaced it with LSTM in the model, denoted as LSTM_SoRec. Notably, the entire CR-SoRec architecture and CR framework remained intact; only the shared BERT was substituted with a shared bidirectional LSTM. The results in Table \\ref{eval_table} confirm that BERT excels in creating intricate user behavior representations by leveraging its bi-directional context learning, thereby boosting recommendation performance.\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Conclusion<\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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This research introduces a novel framework termed \u201cBERT-driven Consistency Regularization for Social Recommendation\u201d (CR-SoRec). We have demonstrated the importance of integrating user context and neighbourhood sampling alongside BERT to create a comprehensive representation for Social Recommendation.

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Within the CR-SoRec framework, BERT contributes by considering bidirectional contexts when predicting forthcoming user-item interactions. In addition to this, we have proposed an inventive approach to enhance the model\u2019s performance. This involves integrating diverse perspectives of user-item interactions and users\u2019 social connections within the Consistency Regularization framework through two tasks: Item CR task and Social CR task.
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Our model consistently outperforms the leading social recommendation algorithms in various experiments across all datasets, highlighting its superiority.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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To know more about Sony Research India\u2019s Research Publications, visit the \u2018Publications\u2019 section on our \u2018Open Innovation\u2019s page:<\/p>

Open Innovation with Sony R&D \u2013 Sony Research India<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

In this blog, Tushar Prakash summarises the paper titled…<\/p>\n","protected":false},"author":1,"featured_media":11248,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[22,17],"tags":[],"class_list":["post-10707","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all-blogs","category-technology","entry"],"yoast_head":"\nDecoding \u2018CR-SoRec: BERT driven Consistency Regularization for Social Recommendation\u2019 - Sony Research India<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/whiteriversmediasolutions.com\/Sony\/decoding-cr-sorec-bert-driven-consistency-regularization-for-social-recommendation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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