عامل بندی ماتریس مبتنی بر تمایلات
ترجمه نشده

عامل بندی ماتریس مبتنی بر تمایلات

عنوان فارسی مقاله: عامل بندی ماتریس مبتنی بر تمایلات با قابلیت اطمینان برای پیشنهاد
عنوان انگلیسی مقاله: Sentiment based matrix factorization with reliability for recommendation
مجله/کنفرانس: سیستم های خبره با کابردهای مربوطه – Expert Systems with Applications
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات
کلمات کلیدی فارسی: فیلتر مشارکتی، عامل بندی ماتریس، سیستم پیشنهاد دهنده، تجزیه و تحلیل تمایلات
کلمات کلیدی انگلیسی: Collaborative filtering، Matrix factorization، Recommender system، Sentiment analysis
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.eswa.2019.06.001
دانشگاه: School of Computer Science, Southwest Petroleum University, Chengdu 610500, China
صفحات مقاله انگلیسی: 10
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 5.891 در سال 2018
شاخص H_index: 162 در سال 2019
شاخص SJR: 1.190 در سال 2018
شناسه ISSN: 0957-4174
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13568
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1. Introduction

2. Preliminaries

3. Proposed algorithm

4. Experiments

5. Conclusions and future work

Declaration of interests

CRediT authorship contribution statement

Acknowledgements

References

بخشی از مقاله (انگلیسی)

Abstract

Recommender systems aim at predicting users’ preferences based on abundant information, such as user ratings, demographics, and reviews. Although reviews are sparser than ratings, they provide more detailed and reliable information about users’ true preferences. Currently, reviews are often used to improve the explainability of recommender systems. In this paper, we propose the sentiment based matrix factorization with reliability (SBMF+R) algorithm to leverage reviews for prediction. First, we develop a sentiment analysis approach using a new star-based dictionary construction technique to obtain the sentiment score. Second, we design a user reliability measure that combines user consistency and the feedback on reviews. Third, we incorporate the ratings, reviews, and feedback into a probabilistic matrix factorization framework for prediction. Experiments on eight Amazon datasets demonstrated that SBMF+R is more accurate than state-of-the-art algorithms.

Introduction

Recommender systems are successful in predicting users’ preferences for items using various techniques, such as content-based (Pazzani & Billsus, 2007) and collaborative filtering (CF)-based (Sarwar, Karypis, Konstan, & Riedl, 2001) approaches. Contentbased approaches extract the features of items and build a model or profile of user interests for recommendation. CF-based approaches predict the interests of users by collecting information from similar users or relevant items. One class of popular CFbased methods is the latent factor model, including matrix factorization (MF) (Koren, Bell, & Volinsky, 2009), non-negative MF (Lee & Seung, 2001), and probabilistic MF (PMF) (Mnih & Salakhutdinov, 2008). CF assumes that the ratings can reflect users’ true preferences and items’ real qualities. However, this assumption does not always accord with real-world scenarios for several reasons. First, critical users tend to give a poor rating, whereas tolerant users might give a good rating, which does not necessarily reflect the quality of the item (Raghavan, Gunasekar, & Ghosh, 2012). Second, users who give the same rating may have different degrees of satisfaction (Cheng, Ding, Zhu, & Kankanhalli, 2018; Lloret, Saggion, & Palomar, 2010). Third, rating noise is introduced by fake likes, that is, superior or bad ratings on products presented by a large group of low-paid workers (Mobasher, Burke, Bhaumik, & Williams, 2007). Fourth, a wrong click also has an implicit influence on the unreliability of ratings. Fig. 1 shows some examples of both reviews and ratings selected from Amazon datasets. Here the reviews given by users are inconsistent with their ratings. Fortunately, the increasing information, such as pictures, tags, reviews, and feedback on e-commerce sites, has shed some light on this problem (McAuley, Targett, Shi, & Hengel, 2015; Yu, Zhou, Deng, & Hu, 2018). Most e-commerce sites allow users to provide reviews of products and give feedback (up or down) to evaluate the usefulness of the reviews (Chen, Qi, & Wang, 2012; Connors, Mudambi, & Schuff, 2011; Hart-Davidson, McLeod, Klerkx, & Wojcik, 2010).