7,654 research outputs found
On User Modelling for Personalised News Video Recommendation
In this paper, we introduce a novel approach for modelling user interests. Our approach captures users evolving information needs, identifies aspects of their need and recommends relevant news items to the users. We introduce our approach within the context of personalised news video retrieval. A news video data set is used for experimentation. We employ a simulated user evaluation
Monte Carlo Methods for Top-k Personalized PageRank Lists and Name Disambiguation
We study a problem of quick detection of top-k Personalized PageRank lists.
This problem has a number of important applications such as finding local cuts
in large graphs, estimation of similarity distance and name disambiguation. In
particular, we apply our results to construct efficient algorithms for the
person name disambiguation problem. We argue that when finding top-k
Personalized PageRank lists two observations are important. Firstly, it is
crucial that we detect fast the top-k most important neighbours of a node,
while the exact order in the top-k list as well as the exact values of PageRank
are by far not so crucial. Secondly, a little number of wrong elements in top-k
lists do not really degrade the quality of top-k lists, but it can lead to
significant computational saving. Based on these two key observations we
propose Monte Carlo methods for fast detection of top-k Personalized PageRank
lists. We provide performance evaluation of the proposed methods and supply
stopping criteria. Then, we apply the methods to the person name disambiguation
problem. The developed algorithm for the person name disambiguation problem has
achieved the second place in the WePS 2010 competition
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
A User Behavior Based Study on Search Engine Ranking
In this era of information explosion, finding convenient ways to get the desired information is becoming ever more vital today. With a review of the existing information retrieval and feedback technology, this paper puts forward a method to establish and update user profile model through obtaining userâs implicit feedbacks. The userâs explicit information is not a must. Instead, this method, with the implicit information acquired by observing the behaviors of the users when browsing web pages, establishes and updates the user profile model and thus reduces the workload.Keywords: Information retrieval?Implicit feedback?Relevance feedback; User profile mode
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