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1
November 2014
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 5
Downloads (6 Weeks): 15, Downloads (12 Months): 149, Downloads (Overall): 534
Full text available:
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Rich contextual information is typically available in many recommendation domains allowing recommender systems to model the subtle effects of context on preferences. Most contextual models assume that the context shares the same latent space with the users and items. In this work we propose CARS2, a novel approach for learning ...
Keywords:
collaborative filtering, latent factor models, learning representations, context-aware recommendation
2
May 2014
ACM Computing Surveys (CSUR): Volume 47 Issue 1, July 2014
Publisher: ACM
Bibliometrics:
Citation Count: 58
Downloads (6 Weeks): 158, Downloads (12 Months): 1,353, Downloads (Overall): 5,877
Full text available:
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Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I ...
Keywords:
applications, collaborative filtering, challenges, recommender systems, social networks, Algorithms, survey
3
October 2013
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 3
Downloads (6 Weeks): 5, Downloads (12 Months): 31, Downloads (Overall): 214
Full text available:
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Recommender systems are frequently used in domains in which users express their preferences in the form of graded judgments, such as ratings. Current ranking techniques are based on one of two sub-optimal approaches: either they optimize for a binary metric such as Average Precision, which discards information on relevance levels, ...
Keywords:
collaborative filtering, latent factor model, top-n recommendation, ranking, recommender systems, graded average precision
4
October 2013
RecSys '13: Proceedings of the 7th ACM conference on Recommender systems
Publisher: ACM
Bibliometrics:
Citation Count: 12
Downloads (6 Weeks): 5, Downloads (12 Months): 73, Downloads (Overall): 275
Full text available:
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Extended Collaborative Less-is-More Filtering xCLiMF is a learning to rank model for collaborative filtering that is specifically designed for use with data where information on the level of relevance of the recommendations exists, e.g. through ratings. xCLiMF can be seen as a generalization of the Collaborative Less-is-More Filtering (CLiMF) method ...
Keywords:
ranking, expected reciprocal rank, collaborative filtering, graded relevance, top-n recommendation
5
October 2013
Content-Based Analysis Of Digital Video focuses on fundamental issues underlying the development of content access mechanisms for digital video. It treats topics that are critical to successfully automating the video content extraction and retrieval processes, and includes coverage of: - Video parsing, - Video content indexing and representation, - Affective ...
6
August 2013
IJCAI '13: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Publisher: AAAI Press
In this paper we tackle the problem of recommendation in the scenarios with binary relevance data, when only a few ( k ) items are recommended to individual users. Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets, or not specifically focused ...
7
July 2013
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction: Volume 4 Issue 3, June 2013
Publisher: ACM
Bibliometrics:
Citation Count: 5
Downloads (6 Weeks): 11, Downloads (12 Months): 53, Downloads (Overall): 389
Full text available:
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Online photo-sharing sites provide a wealth of information about user behavior and their potential is increasing as it becomes ever-more common for images to be associated with location information in the form of geotags. In this article, we propose a novel approach that exploits geotagged images from an online community ...
Keywords:
geotag, nontrivial recommendation, Collaborative filtering, location-based recommendation, social media application
8
April 2013
Information Sciences: an International Journal: Volume 229, April, 2013
Publisher: Elsevier Science Inc.
We propose a novel unified recommendation model, URM, which combines a rating-oriented collaborative filtering (CF) approach, i.e., probabilistic matrix factorization (PMF), and a ranking-oriented CF approach, i.e., list-wise learning-to-rank with matrix factorization (ListRank). The URM benefits from the rating-oriented perspective and the ranking-oriented perspective by sharing common latent features of ...
Keywords:
Matrix factorization, Ranking, Collaborative filtering, Recommender systems, Unified recommendation model
9
February 2013
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
Publisher: ACM
Bibliometrics:
Citation Count: 7
Downloads (6 Weeks): 11, Downloads (12 Months): 114, Downloads (Overall): 978
Full text available:
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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 algorithm based on joint matrix factorization (JMF). We jointly factorize the user-item matrix containing general movie ratings and other contextual ...
Keywords:
Collaborative filtering, context-aware recommendation, matrix factorization, mood-specific movie similarity, recommender systems
10
September 2012
RecSys '12: Proceedings of the sixth ACM conference on Recommender systems
Publisher: ACM
Bibliometrics:
Citation Count: 64
Downloads (6 Weeks): 16, Downloads (12 Months): 186, Downloads (Overall): 1,222
Full text available:
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In this paper we tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets, or not specifically focused on improving ...
Keywords:
learning to rank, matrix factorization, collaborative filtering, less is more, mean reciprocal rank
11
August 2012
SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 18
Downloads (6 Weeks): 5, Downloads (12 Months): 46, Downloads (Overall): 576
Full text available:
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This paper studies result diversification in collaborative filtering. We argue that the diversification level in a recommendation list should be adapted to the target users' individual situations and needs. Different users may have different ranges of interests -- the preference of a highly focused user might include only few topics, ...
Keywords:
collaborative filtering, latent factor model, mean-variance, diversity, portfolio theory
12
June 2008
IEEE Transactions on Multimedia: Volume 10 Issue 4, June 2008
Publisher: IEEE Press
Auditory scenes are temporal audio segments with coherent semantic content. Automatically classifying and grouping auditory scenes with similar semantics into categories is beneficial for many multimedia applications, such as semantic event detection and indexing. For such semantic categorization, auditory scenes are first characterized with either low-level acoustic features or some ...
Keywords:
co-clustering, local feature grouping trends, Audio content analysis, auditory scene categorization
13
January 2008
IEEE Transactions on Multimedia: Volume 10 Issue 1, January 2008
Publisher: IEEE Press
Inspired by classical text document analysis employing the concept of (key) words, this paper presents an unsupervised approach to discover (key) audio elements in general audio documents. The (key) audio elements can be considered the equivalents of the text (key) words, and enable content-based audio analysis and retrieval following the ...
Keywords:
Audio content mining, content-based audio analysis, audio keywords, audio element, key audio element, knowledge discovery
14
March 2007
IEEE Transactions on Circuits and Systems for Video Technology: Volume 17 Issue 3, March 2007
Publisher: IEEE Press
This paper unravels the problem of temporal video segmentation, or video parsing, and explores the possibilities for defining theoretical limits for the expected performance of a general parsing algorithm. In particular, we address the challenge of computing the coherence of video content, which is critical to the ability of an ...
Keywords:
video parsing, video scene detection, Shot boundary detection, video segmentation
15
December 2006
IEEE Transactions on Audio, Speech, and Language Processing: Volume 14 Issue 3, December 2006
Publisher: IEEE Press
Key audio effects are those special effects that play critical roles in human's perception of an auditory context in audiovisual materials. Based on key audio effects, high-level semantic inference can be carried out to facilitate various content-based analysis applications, such as highlight extraction and video summarization. In this paper, a ...
Keywords:
Bayesian network, auditory context, flexible framework, grammar network, key audio effect, multi-background model, Audio content analysis
16
December 2005
IEEE Transactions on Multimedia: Volume 7 Issue 6, December 2005
Publisher: IEEE Press
This paper addresses the challenge of automatically extracting the highlights from sports TV broadcasts. In particular, we are interested in finding a generic method of highlights extraction, which does not require the development of models for the events that are thought to be interpreted by the users as highlights. Instead, ...
Keywords:
Affective video content analysis, video content pruning, video highlights extraction, video abstraction, video content modeling
17
February 2005
IEEE Transactions on Multimedia: Volume 7 Issue 1, February 2005
Publisher: IEEE Press
This paper looks into a new direction in video content analysis - the representation and modeling of affective video content . The affective content of a given video clip can be defined as the intensity and type of feeling or emotion (both are referred to as affect) that are expected ...
18
February 2002
IEEE Transactions on Circuits and Systems for Video Technology: Volume 12 Issue 2, February 2002
Publisher: IEEE Press
Partitioning a video sequence into shots is the first step toward video-content analysis and content-based video browsing and retrieval. A video shot is defined as a series of interrelated consecutive frames taken contiguously by a single camera and representing a continuous action in time and space. As such, shots are ...
19
December 1999
IEEE Transactions on Circuits and Systems for Video Technology: Volume 9 Issue 8, December 1999
Publisher: IEEE Press
Key frames and previews are two forms of a video abstract, widely used for various applications in video browsing and retrieval systems. We propose in this paper a novel method for generating these two abstract forms for an arbitrary video sequence. The underlying principle of the proposed method is the ...
20
June 1999
IEEE Transactions on Circuits and Systems for Video Technology: Volume 9 Issue 4, June 1999
Publisher: IEEE Press
We present a newly developed strategy for automatically segmenting movies into logical story units. A logical story unit can be understood as an approximation of a movie episode, which is a high-level temporal movie segment, characterized either by a single event (dialog, action scene, etc.) or by several events taking ...
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