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Session-Based Recommendations Using Item Embedding

Published: 07 March 2017 Publication History

Abstract

Recent methods for learning vector space representations of words, word embedding, such as GloVe and Word2Vec have succeeded in capturing fine-grained semantic and syntactic regularities. We analyzed the effectiveness of these methods for e-commerce recommender systems by transferring the sequence of items generated by users' browsing journey in an e-commerce website into a sentence of words. We examined the prediction of fine-grained item similarity (such as item most similar to iPhone 6 64GB smart phone) and item analogy (such as iPhone 5 is to iPhone 6 as Samsung S5 is to Samsung S6) using real life users' browsing history of an online European department store. Our results reveal that such methods outperform related models such as singular value decomposition (SVD) with respect to item similarity and analogy tasks across different product categories. Furthermore, these methods produce a highly condensed item vector space representation, item embedding, with behavioral meaning sub-structure. These vectors can be used as features in a variety of recommender system applications. In particular, we used these vectors as features in a neural network based models for anonymous user recommendation based on session's first few clicks. It is found that recurrent neural network that preserves the order of user's clicks outperforms standard neural network, item-to-item similarity and SVD (recall@10 value of 42% based on first three clicks) for this task.

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cover image ACM Conferences
IUI '17: Proceedings of the 22nd International Conference on Intelligent User Interfaces
March 2017
654 pages
ISBN:9781450343480
DOI:10.1145/3025171
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 March 2017

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Author Tags

  1. deep learning
  2. e-commerce
  3. glove
  4. item embedding
  5. recurrent neural network
  6. session-based recommender system
  7. word embedding
  8. word2vec

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IUI '17 Paper Acceptance Rate 63 of 272 submissions, 23%;
Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

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  • (2024)A Personalised Session-Based Recommender System with Sequential Updating Based on Aggregation of Item EmbeddingsIEICE Transactions on Information and Systems10.1587/transinf.2023DAP0006E107.D:5(638-649)Online publication date: 1-May-2024
  • (2024)Improving severity classification of Hebrew PET-CT pathology reports using test-time augmentationJournal of Biomedical Informatics10.1016/j.jbi.2023.104577149(104577)Online publication date: Jan-2024
  • (2023)Challenges for Anonymous Session-Based Recommender Systems in Indoor EnvironmentsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608879(1339-1341)Online publication date: 14-Sep-2023
  • (2023)Counterfactual Adversarial Learning for RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615152(4115-4119)Online publication date: 21-Oct-2023
  • (2023)Learning and Understanding User Interface Semantics from Heterogeneous Networks with Multimodal and Positional AttributesACM Transactions on Interactive Intelligent Systems10.1145/357852213:3(1-31)Online publication date: 11-Sep-2023
  • (2023)Improvement and Optimization of Vulnerability Detection Methods for Ethernet Smart ContractsIEEE Access10.1109/ACCESS.2023.329867211(78207-78223)Online publication date: 2023
  • (2023)SJORS: A Semantic Recommender System for JournalistsBusiness & Information Systems Engineering10.1007/s12599-023-00843-666:6(691-708)Online publication date: 21-Dec-2023
  • (2022)Learning User Interface Semantics from Heterogeneous Networks with Multimodal and Positional AttributesProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511143(433-446)Online publication date: 22-Mar-2022
  • (2022)Boosting Item Coverage in Session-Based RecommendationServices Computing – SCC 202210.1007/978-3-031-23515-3_8(101-118)Online publication date: 21-Dec-2022
  • (2022)Interact2Vec: Neural Item and User Embedding for Collaborative FilteringIntelligent Systems10.1007/978-3-031-21689-3_35(494-509)Online publication date: 19-Nov-2022
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