Ratings meet reviews, a combined approach to recommend

Ling, Guang Sheng; Lyu, Michael R.; King, Irwin

doi:10.1145/2645710.2645728

Cited by 289 publications

(215 citation statements)

References 23 publications

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“…The typical side information includes social relationship, social tag, item description, etc. Our work is closely related to tag-based recommendation, review-based recommendation and item description-based recommendation (Agarwal and Chen, 2010;Liang et al, 2010;Gemmell et al, 2011;Ling et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, there are several works that incorporated the review text into recommendation algorithms (McAuley and Leskovec, 2013;Bao et al, 2014;Diao et al, 2014;Ling et al, 2014). In McAuley and Leskovec (2013), the authors proposed a model named hidden factors as topics (HFT), which combined the objectives of matrix factorization and topic modeling together.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, our proposed model J-UCR (see Section 4.2) is designed to jointly generate the user-specific and item-specific topic distributions. In Bao et al (2014), Diao et al (2014), and Ling et al (2014), all the proposed algorithms tried to model reviews and ratings in one generative process. Note that, all of the works only investigate the function of reviews in a recommender system, but ignore other kinds of UGC.…”

Section: Related Workmentioning

confidence: 99%

“…Although in some previous works (Liang et al, 2010;Zanardi and Capra, 2011;McAuley and Leskovec, 2013;Ling et al, 2014), UGC has been verified to be helpful to improve the prediction accuracy in recommender systems, there are still three challenging problems:…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Collaborative recommendation with user generated content

Yin

2015

Engineering Applications of Artificial Intelligence

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Collaborative recommendation with user generated content

Yin

2015

Engineering Applications of Artificial Intelligence

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“…This is particularly important for deep models such as RNNs, since these high-capacity models are prone to overfitting. Existing hybrid methods have used unsupervised learning objectives on text content to regularize the parameters of the recommendation model [4,23,24]. However, since we consume the text directly as an input for prediction, we can not use this approach.…”

Section: Introductionmentioning

confidence: 99%

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Bansal

Belanger

McCallum

2016

Proceedings of the 10th ACM Conference on Recommender Systems

200

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In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.

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Recommending books to be exchanged online in the absence of wish lists

Pera

2017

Asso for Info Science & Tech

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An online exchange system is a web service that allows communities to trade items without the burden of manually selecting them, which saves users' time and effort. Even though online book‐exchange systems have been developed, their services can further be improved by reducing the workload imposed on their users. To accomplish this task, we propose a recommendation‐based book exchange system, called EasyEx, which identifies potential exchanges for a user solely based on a list of items the user is willing to part with. EasyEx is a novel and unique book‐exchange system because unlike existing online exchange systems, it does not require a user to create and maintain a wish list, which is a list of items the user would like to receive as part of the exchange. Instead, EasyEx directly suggests items to users to increase serendipity and as a result expose them to items which may be unfamiliar, but appealing, to them. In identifying books to be exchanged, EasyEx employs known recommendation strategies, that is, personalized mean and matrix factorization, to predict book ratings, which are treated as the degrees of appeal to a user on recommended books. Furthermore, EasyEx incorporates OptaPlanner, which solves constraint satisfaction problems efficiently, as part of the recommendation‐based exchange process to create exchange cycles. Experimental results have verified that EasyEx offers users recommended books that satisfy the users' interests and contributes to the item‐exchange mechanism with a new design methodology.

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Ratings meet reviews, a combined approach to recommend

Cited by 289 publications

References 23 publications

Collaborative recommendation with user generated content

Collaborative recommendation with user generated content

Ask the GRU

Recommending books to be exchanged online in the absence of wish lists

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