Paper Info
Title
Opinions matter: a general approach to user profile modeling for contextual suggestion
Abstract
The increasing use of mobile devices enables an information retrieval (IR) system to capitalize on various types of contexts (e.g., temporal and geographical information) about its users. Combined with the user preference history recorded in the system, a better understanding of users' information need can be achieved and it thus leads to improved user satisfaction. More importantly, such a system could proactively recommend suggestions based on the contexts. User profiling is essential in contextual suggestion. However, given most users' observed behaviors are sparse and their preferences are latent in an IR system, constructing accurate user profiles is generally difficult. In this paper, we focus on location-based contextual suggestion and propose to leverage users' opinions to construct the profiles. Instead of simply recording \"what places a user likes or dislikes\" in the past (i.e., description-based profile), we want to construct a profile to identify \"why a user likes or dislikes a place\" so as to better predict whether the user would like a new candidate suggestion of place. By assuming users would like or dislike a place with similar reasons, we construct the opinion-based user profile in a collaborative way: opinions from the other users are leveraged to estimate a profile for the target user. Candidate suggestions are represented in the same fashion and ranked based on their similarities with respect to the user profiles. Moreover, we also develop a novel summary generation method that utilizes the opinion-based user profiles to generate personalized and high-quality summaries for the suggestions. Experiments are conducted over three standard TREC contextual suggestion collections and a Yelp data set. Extensive experiment comparisons confirm that the proposed opinion-based user modeling outperforms the existing description-based methods. In particular, the systems developed based on the proposed methods have been ranked as top 1 in both TREC 2013 and 2014 contextual suggestion tracks.
Year
DOI
Venue
2015
10.1007/s10791-015-9278-7
Inf. Retr. Journal
Keywords
Field
DocType
Contextual suggestions,Opinions,User modeling,Recommendation
Data mining,Information needs,User profile,Ranking,Information retrieval,Computer science,Profiling (computer programming),Mobile device,User modeling,Computer user satisfaction
Journal
Volume
Issue
ISSN
18
6
1386-4564
Citations 
PageRank 
References 
5
0.47
36
Authors
4
Name
Order
Citations
PageRank
Peilin Yang110012.00
Hongning Wang292554.89
Hui Fang391863.03
Deng Cai47938320.26