Social temporal collaborative ranking for context aware movie recommendation

Liu, Nathan N.; He, Luheng; Zhao, Min

doi:10.1145/2414425.2414440

Cited by 60 publications

(33 citation statements)

References 30 publications

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“…For future works, we are interested in generalizing our HGMF in two aspects, including (i) collectively mining complex structure correlations from heterogeneous domains [3], and (ii) incorporating auxiliary data such as social networks and mobile context [7,8]. …”

Section: Discussionmentioning

confidence: 99%

HGMF

Wang

Pan

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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Matrix factorization is one of the most powerful techniques in collaborative filtering, which models the (user, item) interactions behind historical explicit or implicit feedbacks. However, plain matrix factorization may not be able to uncover the structure correlations among users and items well such as communities and taxonomies. As a response, we design a novel algorithm, i.e., hierarchical group matrix factorization (HGMF), in order to explore and model the structure correlations among users and items in a principled way. Specifically, we first define four types of correlations, including (user, item), (user, item group), (user group, item) and (user group, item group); we then extend plain matrix factorization with a hierarchical group structure; finally, we design a novel clustering algorithm to mine the hidden structure correlations. In the experiments, we study the effectiveness of our HGMF for both rating prediction and item recommendation, and find that it is better than some stateof-the-art methods on several real-world data sets.

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Section: Discussionmentioning

confidence: 99%

HGMF

Wang

Pan

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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“…A collaborative filtering algorithm, CLiMF [25] directly optimizes the evaluation metric of Mean Reciprocal Rank while assessing the recommendation process. Context-aware movie recommendation [28] has presented a social, temporal collaborative ranking based matrix factorization to ensure the time-aware recommendation based on the explicit and implicit feedback of the users. To tackle the unexpectedness in the observed data, the serendipitous personalized ranking method suggests the effective recommendation with the help of matrix factorization and improves the serendipity as well as recommendation accuracy [29].…”

Section: Ranking Based Recommender Systemsmentioning

confidence: 99%

Diversity-Ensured Semantic Movie Recommendation by Applying Linked Open Data

Srinivasan¹,

Mani²

2018

IJIES

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Abstract:The recommender system becomes a significant research area due to the popularity of the social web. Traditional semantic recommender systems deliver poor performance when balancing the recommendation accuracy and diversity. Also, the rank-based recommendation methods lack to obtain the coverage of the entire preferences of the user in the top-N recommendation list. Thus, this paper presents the Diversity-Ensured Semantic-aware Item REcommendation (DESIRE) that deals with the consistent and reliable knowledge source to significantly improve the quality and provide the diversity-ensured top-N recommendation list. The DESIRE approach builds the semantically relevant graphs such as movie-centric and user rating-centric graph with the help of both the Linked Open Data (LOD) and the explicit ratings of the users. By extracting the semantic-path based features from the user rating-centric graph, it executes the ranking algorithm for the top-N movie recommendation. Moreover, the diversity-aware re-ranking tends to maintain the trade-off between the diversity and accuracy in the top-N recommendation.

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“…The method proposed in this work is applied on a sparse, large-scale dataset, and the particular characteristics of the dataset are extracted and utilized. Liu et al [34] present a social temporal collaborative ranking model that can simultaneously achieve three objectives: (1) combines both explicit and implicit user feedback, (2) supports time awareness using an expressive sequential matrix factorization model and a temporal smoothness regularization function to tackle overfitting, and (3) supports social network awareness by incorporating a network regularization term. Dias and Fonseca [35] explore the usage of temporal context and session diversity in session-based CF techniques for music recommendation.…”

Section: Related Workmentioning

confidence: 99%

Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2017

CSIMQ

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Abstract. Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user's rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users' ratings is inadequate, since it fails to follow such shifts in users' rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in useruser collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users' rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.

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Social temporal collaborative ranking for context aware movie recommendation

Cited by 60 publications

References 30 publications

HGMF

HGMF

Diversity-Ensured Semantic Movie Recommendation by Applying Linked Open Data

Using Time Clusters for Following Users’ Shifts in Rating Practices

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