Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)

Diao, Qiming; Qiu, Minghui; Wu, Chao-Yuan; Smola, Alexander J.; Jiang, Jing; Wang, Chong

doi:10.1145/2623330.2623758

Cited by 364 publications

(227 citation statements)

References 19 publications

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“…(6) We compare with a state-of-the-art recommendation algorithm JMARS (Diao et al, 2014), which utilizes user and aspects of a review with collaborative filtering and topic modeling.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…We compare to neural network models such as paragraph vector (Le and Mikolov, 2014), convolutional neural network, and baselines such as feature-based SVM (Pang et al, 2002), recommendation algorithm JMARS (Diao et al, 2014). Experimental results show that: (1) the proposed neural model shows superior performances over all baseline algorithms; (2) gated recurrent neural network dramatically outperforms standard recurrent neural network in document modeling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Document Modeling with Gated Recurrent Neural Network for Sentiment Classification

Tang

Qin

Liu

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

1,284

761

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Document level sentiment classification remains a challenge: encoding the intrinsic relations between sentences in the semantic meaning of a document. To address this, we introduce a neural network model to learn vector-based document representation in a unified, bottom-up fashion. The model first learns sentence representation with convolutional neural network or long short-term memory. Afterwards, semantics of sentences and their relations are adaptively encoded in document representation with gated recurrent neural network. We conduct document level sentiment classification on four large-scale review datasets from IMDB and Yelp Dataset Challenge. Experimental results show that: (1) our neural model shows superior performances over several state-of-the-art algorithms; (2) gated recurrent neural network dramatically outperforms standard recurrent neural network in document modeling for sentiment classification. 1

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“…(6) We compare with a state-of-the-art recommendation algorithm JMARS (Diao et al, 2014), which utilizes user and aspects of a review with collaborative filtering and topic modeling.…”

Section: Baseline Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Document Modeling with Gated Recurrent Neural Network for Sentiment Classification

Tang

Qin

Liu

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

1,284

761

View full text Add to dashboard Cite

show abstract

“…We use a database of 2,000 movies and 100 users extracted from IMDb (a subset of the data described in [4]) for our method. Our method works in three steps.…”

Section: Methodsmentioning

confidence: 99%

“…The corpus of online reviews represents a source of rich meta-data for each movie. We use a database of 2000 movies and 100 users extracted from IMDb by Diao et al [4] as a proof of concept in this paper. The challenge we face is to mine the noisy free-text reviews from a heterogeneous set of people to extract meaningful and personalized sublists of movies.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Crowd Sourced Reviews to Explain Movie Recommendation

et al. 2016

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Streaming services such as Netflix, M-Go, and Hulu use advanced recommender systems to help their customers identify relevant content quickly and easily. These recommenders display the list of recommended movies organized in sublists labeled with the genre or some more specific labels. Unfortunately, existing methods to extract these labeled sublists require human annotators to manually label movies, which is time-consuming and biased by the views of annotators. In this paper, we design a method that relies on crowd sourced reviews to automatically identify groups of similar movies and label these groups. Our method takes the content of movie reviews available online as input for an algorithm based on Latent Dirichlet Allocation (LDA) that identifies groups of similar movies. We separate the set of similar movies that share the same combination of genre in sublists and personalize the movies to show in each sublist using matrix factorization. The results of a side-by-side comparison of our method against Technicolor's M-Go VoD service are encouraging.

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“…The latent factor of item i is denoted as V i and stored as the ith row of item factor matrix V . To learn the latent factors of users and items, [28] employs probabilistic matrix factorization to factor the user-item matrix into the product of user and item latent factors. The conditional probability of the observed ratings is defined as the following equation:…”

Section: Prediction With Social Network Informationmentioning

confidence: 99%

Prediction of purchase behaviors across heterogeneous social networks

Wang

Liu

et al. 2015

J Supercomput

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Due to the development of web services, many social network sites, as well as online shopping sites have been booming in the past decade, where it is a common phenomenon that people are likely to use multiple services at the same time. On the one hand, previous research findings indicate the data sparsity issues of online shopping accounts, which is caused by the heavy-tailed distribution of user information. On the other hand, in social network sites, the personal information and the corresponding statuses of an account are abundant, and their genuineness is guaranteed either by the service provider, or by the willingness of the account owner to connect to his or her friends in reality. Making use of the correlation between accounts of a same individual is a crucial prerequisite for many interesting cross network applications, such as improving the recommendation performance of the online shopping sites using extra information from social network services. In this paper, we firstly propose a game-theoretic method to identify correlation accounts of individuals between social network sites and online shopping sites with stable matching model, incorporating account profiles as well as historical behaviors. Using the above account relationships, we then put forward a predicting method that combines heterogeneous social network information and online shopping information, to predict the purchasing behaviors. The results show that our method identifies up to 70 % of the correlation accounts between Facebook and eBay, one of the most popular social network sites and online shopping 123Prediction of purchase behaviors across heterogeneous... 3321 sites in the world, respectively. The experimental results also show that using the correlation account sets, the accuracy of our purchase predicting method outperforms the state-of-the-art methods by 5 %.

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Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)

Cited by 364 publications

References 19 publications

Document Modeling with Gated Recurrent Neural Network for Sentiment Classification

Document Modeling with Gated Recurrent Neural Network for Sentiment Classification

Exploiting Crowd Sourced Reviews to Explain Movie Recommendation

Prediction of purchase behaviors across heterogeneous social networks

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