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The remainder of the article is structured as follows: Sect. 2 reviews the literature in the field briefly, and Sect. 3 provides necessary background information about recommender systems and major deep learning techniques. Section 4 reveals a perspectival synopsis of applied deep learning methodologies within the context of recommender systems.
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 · Customer Reviews: 4.2 out of 5 stars 15 customer ratings; Amazon Best Sellers Rank: #1,210,044 in Books (See Top 100 in Books) #104 in Artificial Intelligence Expert Systems #3348 in Databases & Big Data #875 in Database Storage & Design
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Recommender Systems Review of Types, Techniques, and Applications: 10.4018/978-1-4666-5888-2.ch714: Recommender or recommendation systems are software tools that make useful suggestions to users, by taking into account their profile, preferences and/or
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 · Chen et al. Recommender systems based on user reviews: the state of the art. USER MODEL USER-ADAP, 2015. Xu et al. Social networking meets recommender systems: survey. Int.J.Social Network Mining, 2015. Yu et al. A survey of point-of-interest recommendation in location-based social networks. In Workshops at AAAI, 2015.
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Customer Reviews: 4.2 out of 5 stars 3 customer ratings; Amazon Best Sellers Rank: #2,285,769 in Books (See Top 100 in Books) #676 in Network Storage & Retrieval Administration #780 in Artificial Intelligence (Books) #2568 in Artificial Intelligence & Semantics
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Harvard Business School Review, 1993(2): pp. 64-73. Google Scholar; Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl 1994. Grouplens: An open architecture for collaborative filtering of nemews. In Proceedings of A CM CSCW'94 Conference on Computer- Supported Cooperative Work, pp 175-186.
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In recent years, different types of review systems have been developed with the recommender system (RS). RSs are developed based on user textual reviews, ratings, and comparative opinions.
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In this paper, we provide a comprehensive review of the related research contents of deep learning based recommender systems. Firstly, we introduce the basic terminologies and background concepts ...
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Chen et al. Recommender systems based on user reviews: the state of the art. USER MODEL USER-ADAP, 2015. Xu et al. Social networking meets recommender systems: survey. Int.J.Social Network Mining, 2015. Yu et al. A survey of point-of-interest recommendation in location-based social networks. In Workshops at AAAI, 2015.
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 · Peer-review under responsibility of KES International 1454 Nitin Pradeep Kumar and Zhenzhen Fan / Procedia Computer Science 60 ( 2015 ) 1453 – 1461 shopping, news and social networking sites make use of such algorithm to recommend movies, books, articles, or …
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 · Zhao, G. et al. Exploring users’ internal influence from reviews for social recommendation. IEEE Trans. Multimedia 21 (3), 771–781 (2018). ADS Google Scholar
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 · ScienceDirect Available online at www.sciencedirect.com Procedia Computer Science 130 (2018) 214–221 1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs. 10.1016/j.procs.2018.04.032 10.1016/j.procs.2018.04.032 © 2018 The Authors.
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 · Ma H, Zhou D, and Liu C, et. al. Recommender systems with social regularization. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011, Hong Kong, China, February 9-12, 2011: 287-296.
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 · Introduction. It is a common practice in the technologies being used for recommender systems to use customer's opinion and experiences for recommending desired products to the users (Aciar et al., 2006).The customers’ opinions are generally available in an unstructured and unguided format which requires an extra effort in processing these reviews to get some fruitful conclusion from it.
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user ui likes a review issued by another user ut, user ui prob-ably will add user ut to his/her trust list. This process of trust generation is a unilateral action that does not require user ut to confirm the relationship. This also indicates that user ui does not need to even know user ut in the real life.
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 · Section 2 briefly reviews related work in POI recommender systems. Section 3 introduces the preliminary knowledge used in this paper. Section 4 describes the details of our proposed POI recommendation algorithm. Experiments are evaluated in Section 5. Finally, we conclude this paper and present some directions for future work in Section 6. 2.
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Unlike expertise location systems which users query actively when looking for an expert, expert recommender systems suggest individuals without the context of …
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Customer Reviews: 5.0 out of 5 stars 2 customer ratings; Amazon Best Sellers Rank: #2,625,819 in Books (See Top 100 in Books) #632 in E-Commerce (Books) #455 in Computer Programming Structured Design #640 in Data Warehousing (Books)
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T raditional recommender systems, such as those based on content-based and collaborative filtering, tend to use fair-ly simple user models. For example, user-based collabora-
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Liang Zhang /Journal of Engineering Science and T e chnology Review 6 (3) (2013) 1 41 - 145 145 Un popular item s a ctive users like d have greater reference
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performing a detailed review of a limited number of mature works –e.g., those works that present a system imple- mentation and / or an exhaustive ev aluation–, or a broad review of works ...
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US7809601B2 US09/981,516 US98151601A US7809601B2 US 7809601 B2 US7809601 B2 US 7809601B2 US 98151601 A US98151601 A US 98151601A US 7809601 B2 US7809601 B2 US 7809601B2 Authority US United States Prior art keywords product consumer data products invention Prior art date 2000-10-18 Legal status (The legal status is an assumption and is not a legal conclusion.
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Link prediction is an attractive research topic in the field of data mining and has significant applications in improving performance of recommendation system and exploring evolving mechanisms of ...
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Aciar S, Zhang D, Simoff S, et al. Recommender System Based on Consumer Product Reviews[C]// IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Computer Society, 2007:719-723.
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A recommendations service recommends items to individual users based on a set of items that are known to be of interest to the user, such as a set of items previously purchased by the user. In the disclosed embodiments, the service is used to recommend products to users of a merchant's Web site. The service generates the recommendations using a previously-generated table which maps items to ...
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US9224427B2 US11/695,327 US69532707A US9224427B2 US 9224427 B2 US9224427 B2 US 9224427B2 US 69532707 A US69532707 A US 69532707A US 9224427 B2 US9224427 B2 US 9224427B2 Authority US United States Prior art keywords recommendation media item server rating play Prior art date 2007-04-02 Legal status (The legal status is an assumption and is not a legal conclusion.
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Ma, Hao, et al. "Recommender systems with social regularization." Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011. TrustSVD[Guo et al. 2015] Guo, Guibing, Jie Zhang, and Neil Yorke-Smith. "TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item ...
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1.2 Traditional Collaborative Filtering. Traditional collaborative filtering (TCF) is the most popular recommendation technique and is widely adopted in many commercial domains including businesses, such as Amazon [], TiVo and Yahoo![], while, in [], the GroupLens system used TCF for NetNewsThe TCF methods are mainly divided into two main categories: memory-based [29–31] and model-based [32 ...
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