Modeling Temporal Dynamics of User Preferences in Movie Recommendation

Tahmasbi, Hamidreza; Jalali, Mehrdad; Shakeri, Hassan

doi:10.1109/iccke.2018.8566316

Cited by 7 publications

(1 citation statement)

References 26 publications

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“…Li et al [27] used movie feature vector combined with the user rating matrix to generate the user interest vector. Tahmasbi et al [28] gave an approach to model temporal dynamics of user preferences in movie recommendation systems based on a coupled tensor factorization framework. Middleton et al [29] explored the acquisition of user profiles using browsing behaviour and presented an ontological representation to extract user preferences.…”

Section: Related Workmentioning

confidence: 99%

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

Kumar

Al‐Turjman³

et al. 2020

IEEE Access

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Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules namely candidate feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment and recommendation module. Review corpus is given as an input to the CISER model. Candidate feature extraction module uses context and sentiment confidence to extract features of importance. To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their opinion according to their credibility. The user interest mining module uses aesthetics of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module scores candidate features present in review based on their fastText sentiment polarity. Finally, the recommendation module uses credibility weighted sentiment scoring of user preferred features for purchase recommendations. The proposed recommendation methodology harnesses not only numeric ratings, but also sentiment expressions associated with features, customer preference profile and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93% and MAP@3 is 49%, which is better than current state-of-the-art systems.

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Section: Related Workmentioning

confidence: 99%