[Purpose/significance] In the Web2.0 era, the quality of online reviews is uneven and overloaded; the cognitive cost of getting valuable content from them is increasing. In the paper, we proposed a review recommendation ranking scheme which focuses on the information quality of reviews and emphasizes more on the satisfaction of users' personal information needs. [Method/process] The probabilistic topic model was adopted in this study, user-profile and review model based on the topic model were generated by employing the Word2Vector. By incorporating them into review helpfulness evaluation system, the comment recommendation which integrates user interest and comment quality was realized. The models and related methods were tested and evaluated by a serial of systematic experiments. [Result/conclusion] The results indicate that both information quality and individual information interests are of influence on review perceived helpfulness. The "combining-specific" recommendation strategy, integrating the two factors effectively, performs better than the "interest-specific" and the "utility-specific" recommendation method. From the perspective of information service, the "combining" strategy should be with the high-priority for review recommendation.
Nie Hui
,
Qiu Yifei
. Integrating User Interests with Review Helpfulness for Review Recommendation[J]. Library and Information Service, 2021
, 65(10)
: 68
-78
.
DOI: 10.13266/j.issn.0252-3116.2021.10.008
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