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ACM Transactions on Information Systems (TOIS), Volume 35 Issue 2, December 2016

Cross-Lingual Topic Discovery From Multilingual Search Engine Query Log
Di Jiang, Yongxin Tong, Yuanfeng Song
Article No.: 9
DOI: 10.1145/2956235

Today, major commercial search engines are operating in a multinational fashion to provide web search services for millions of users who compose search queries by different languages. Hence, the search engine query log, which serves as the...

Ranking-Oriented Collaborative Filtering: A Listwise Approach
Shuaiqiang Wang, Shanshan Huang, Tie-Yan Liu, Jun Ma, Zhumin Chen, Jari Veijalainen
Article No.: 10
DOI: 10.1145/2960408

Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking...

Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation
Hongzhi Yin, Bin Cui, Xiaofang Zhou, Weiqing Wang, Zi Huang, Shazia Sadiq
Article No.: 11
DOI: 10.1145/2873055

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users travel out of town. However, the extreme sparsity of a user-POI matrix creates a severe...

Boosting Recommendation in Unexplored Categories by User Price Preference
Jia Chen, Qin Jin, Shiwan Zhao, Shenghua Bao, Li Zhang, Zhong Su, Yong Yu
Article No.: 12
DOI: 10.1145/2978579

State-of-the-art methods for product recommendation encounter a significant performance drop in categories where a user has no purchase history. This problem needs to be addressed since current online retailers are moving beyond single category...

Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains
Liang Hu, Longbing Cao, Jian Cao, Zhiping Gu, Guandong Xu, Dingyu Yang
Article No.: 13
DOI: 10.1145/2976737

In the real-world environment, users have sufficient experience in their focused domains but lack experience in other domains. Recommender systems are very helpful for recommending potentially desirable items to users in unfamiliar domains, and...

Context Trees: Augmenting Geospatial Trajectories with Context
Alasdair Thomason, Nathan Griffiths, Victor Sanchez
Article No.: 14
DOI: 10.1145/2978578

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data...

Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees
Domenico Dato, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini
Article No.: 15
DOI: 10.1145/2987380

Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very effective for scoring query results returned by large-scale Web search engines. Unfortunately, the computational cost of scoring thousands of...