诺库维普

Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text 2018-01-25 15:21:33

In order to recommend products to users, we must ultimately predict how a user will respond to a new product. To do so we must uncover the implicit tastes of each user as well as the properties of each product. For example, in order to predict whether a user will enjoy Harry Potter, it helps to iden... || recommender systems, topic models, librec; || Julian McAuley; Jure Leskovec...

机票个性化推荐方法 2018-01-14 20:57:19

随着互联网的发展以及大数据时代的到来,信息爆炸所带来的信息过载的问题也越发明显。推荐系统作为解决信息过载问题的一个有效解决方案,能够有效地为用户个性化地推荐其感兴趣的产品与信息,其在过去数十年中也逐渐成为一个重要的研究热点并被广泛地应用到工业领域。论文主要研究推荐系统在机票个性化推荐问题中的应用。与传统的推荐系统的推荐对象,如电影、书籍等具有相对固定属性的静态商品不同,机票是属于易受时间影响的,且价格敏感的动态商品。同一张机票在距离起飞的不同时间有着较大的价格波动,而不同的机票价格波动将直接影响用户的购买行为。文中通过研究和分析用户的历史机票订单数据特征,提出了一种基于用户偏好... || 硕士毕业论文；机票个性化推荐,推荐算法,隐式反馈,协同过滤,选择模型; || 杨芳洲；曹健...

Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis 2018-01-11 17:13:51

Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of onlin... || Recommender Systems; Sentiment Analysis; Collaborative Filtering; Recommendation Explanation; EFM; || Yongfeng Zhang...

Optimizing the Use of Gene Expression Profiling in Early-Stage Breast Cancer 2017-12-28 11:01:43

Gene expression profiling assays are frequently used to guide adjuvant chemotherapy decisions in hormone receptor–positive, lymph node–negative breast cancer. We hypothesized that the clinical value of these new tools would be more fully realized when appropriately integrated with high-quality clini... || 21gene 预测；预测模型中的假设检验；乳腺癌症；医学机器学习; || Hyun-seok Kim...

TLRec:Transfer Learning for Cross-domain Recommendation 2017-12-17 22:17:48

In the era of big data, the available information on the Internet has overwhelmed the human processing capabilities in some commercial applications. Recommendation techniques are indispensable to predict user ratings on items in terms of historical data and deal with the information overload. In man... || transfer learning; cross-domain; partially overlapped; || Leihui Chen, Jianbing Zheng...

Deep Cross-Domain Fashion Recommendation 2017-12-14 21:47:44

With the increasing number of online shopping services, the number of users and the quantity of visual and textual information on the Internet, there is a pressing need for intelligent recommendation systems that analyze the user’s behavior amongst multiple domains and help them to find the desirabl... || Fashion recommendation; deep learning; cross-domain knowledge transfer; transfer learning; domain adaptation; CNN; || Shatha Jaradat...

SCRAM: A Sharing Considered Route Assignment Mechanism for Fair Taxi Route Recommendations 2017-12-13 16:59:48

Recommending routes for a group of competing taxi drivers is almost untouched in most route recommender systems. For this kind of problem, recommendation fairness and driving efficiency are two fundamental aspects. In the paper, we propose SCRAM, a sharing considered route assignment mechanism for f... || Recommender Systems; Assignment Mechanism; Fairness; Taxis; || Shiyou Qian; Jian Cao...

WWW 23

Austin, Texas, USA,

AAAI 23

Washington, DC, USA,

SIGIR2022

Madrid, Spain, July 11th to 15th, 2022

WSDM2023

TBD,

IJCAI2022

Austria, July 23-29, 2022