Intelligent Systems and Technology (TIST)


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ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context, Volume 4 Issue 1, January 2013

Section: 1 - Special Section on Twitter and Microblogging Services

Introduction to the special section on twitter and microblogging services
Irwin King, Wolfgang Nejdl
Article No.: 1
DOI: 10.1145/2414425.2414426

A content-driven framework for geolocating microblog users
Zhiyuan Cheng, James Caverlee, Kyumin Lee
Article No.: 2
DOI: 10.1145/2414425.2414427

Highly dynamic real-time microblog systems have already published petabytes of real-time human sensor data in the form of status updates. However, the lack of user adoption of geo-based features per user or per post signals that the promise of...

Named entity recognition for tweets
Xiaohua Liu, Furu Wei, Shaodian Zhang, Ming Zhou
Article No.: 3
DOI: 10.1145/2414425.2414428

Two main challenges of Named Entity Recognition (NER) for tweets are the insufficient information in a tweet and the lack of training data. We propose a novel method consisting of three core elements: (1) normalization of tweets; (2) combination...

Improving recency ranking using twitter data
Yi Chang, Anlei Dong, Pranam Kolari, Ruiqiang Zhang, Yoshiyuki Inagaki, Fernanodo Diaz, Hongyuan Zha, Yan Liu
Article No.: 4
DOI: 10.1145/2414425.2414429

In Web search and vertical search, recency ranking refers to retrieving and ranking documents by both relevance and freshness. As impoverished in-links and click information is the the biggest challenge for recency ranking, we advocate the...

Lexical normalization for social media text
Bo Han, Paul Cook, Timothy Baldwin
Article No.: 5
DOI: 10.1145/2414425.2414430

Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this article, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalizing...

Reorder user's tweets
Keyi Shen, Jianmin Wu, Ya Zhang, Yiping Han, Xiaokang Yang, Li Song, Xiao Gu
Article No.: 6
DOI: 10.1145/2414425.2414431

Twitter displays the tweets a user received in a reversed chronological order, which is not always the best choice. As Twitter is full of messages of very different qualities, many informative or relevant tweets might be flooded or displayed at...

Section: 2 - Special Section on Social Recommender Systems

Introduction to the special section on social recommender systems
Ido Guy, Li Chen, Michelle X. Zhou
Article No.: 7
DOI: 10.1145/2414425.2414432

Social factors in group recommender systems
Lara Quijano-Sanchez, Juan A. Recio-Garcia, Belen Diaz-Agudo, Guillermo Jimenez-Diaz
Article No.: 8
DOI: 10.1145/2414425.2414433

In this article we review the existing techniques in group recommender systems and we propose some improvement based on the study of the different individual behaviors when carrying out a decision-making process. Our method includes an analysis of...

Generating virtual ratings from chinese reviews to augment online recommendations
Weishi Zhang, Guiguang Ding, Li Chen, Chunping Li, Chengbo Zhang
Article No.: 9
DOI: 10.1145/2414425.2414434

Collaborative filtering (CF) recommenders based on User-Item rating matrix as explicitly obtained from end users have recently appeared promising in recommender systems. However, User-Item rating matrix is not always available or very sparse in...

An approach to social recommendation for context-aware mobile services
Claudio Biancalana, Fabio Gasparetti, Alessandro Micarelli, Giuseppe Sansonetti
Article No.: 10
DOI: 10.1145/2414425.2414435

Nowadays, several location-based services (LBSs) allow their users to take advantage of information from the Web about points of interest (POIs) such as cultural events or restaurants. To the best of our knowledge, however, none of these provides...

Improving recommendation accuracy based on item-specific tag preferences
Fatih Gedikli, Dietmar Jannach
Article No.: 11
DOI: 10.1145/2414425.2414436

In recent years, different proposals have been made to exploit Social Web tagging information to build more effective recommender systems. The tagging data, for example, were used to identify similar users or were viewed as additional information...

A modified random walk framework for handling negative ratings and generating explanations
Yu-Chih Chen, Yu-Shi Lin, Yu-Chun Shen, Shou-De Lin
Article No.: 12
DOI: 10.1145/2414425.2414437

The concept of random walk (RW) has been widely applied in the design of recommendation systems. RW-based approaches are effective in handling locality problem and taking extra information, such as the relationships between items or users, into...

Section: 3 - Special Section on CAMRa2010: Movie Recommendation in Context

Introduction to special section on CAMRa2010: Movie recommendation in context
Alan Said, Shlomo Berkovsky, Ernesto W. De Luca
Article No.: 13
DOI: 10.1145/2414425.2414438

The challenge and workshop on Context-Aware Movie Recommendation (CAMRa2010) were conducted jointly in 2010 with the Recommender Systems conference. The challenge focused on three context-aware recommendation scenarios: time-based, mood-based, and...

An empirical comparison of social, collaborative filtering, and hybrid recommenders
Alejandro Bellogín, Iván Cantador, Fernando Díez, Pablo Castells, Enrique Chavarriaga
Article No.: 14
DOI: 10.1145/2414425.2414439

In the Social Web, a number of diverse recommendation approaches have been proposed to exploit the user generated contents available in the Web, such as rating, tagging, and social networking information. In general, these approaches naturally...

Social temporal collaborative ranking for context aware movie recommendation
Nathan N. Liu, Luheng He, Min Zhao
Article No.: 15
DOI: 10.1145/2414425.2414440

Most existing collaborative filtering models only consider the use of user feedback (e.g., ratings) and meta data (e.g., content, demographics). However, in most real world recommender systems, context information, such as time and social...

Mining contextual movie similarity with matrix factorization for context-aware recommendation
Yue Shi, Martha Larson, Alan Hanjalic
Article No.: 16
DOI: 10.1145/2414425.2414441

Context-aware recommendation seeks to improve recommendation performance by exploiting various information sources in addition to the conventional user-item matrix used by recommender systems. We propose a novel context-aware movie recommendation...

Mathematical description and analysis of adaptive risk choice behavior
Isamu Okada, Hitoshi Yamamoto
Article No.: 17
DOI: 10.1145/2414425.2414442

Which risk should one choose when facing alternatives with different levels of risk? We discuss here adaptive processes in such risk choice behavior by generalizing the study of Roos et al. [2010]. We deal with an n-choice game in...