88,406 research outputs found
Enhanced web log based recommendation by personalized retrieval
University of Technology, Sydney. Faculty of Engineering and Information Technology.With the rapid development of the Internet and WWW, it is more and more important for people to access quality web information. Thus the problem of enabling users to quickly and accurately find information has become an urgent issue. As one of the basic ways to solve this problem, personalized information services have been focusing on fulfilling the personalized information requirements of different users based on their actual demands, preference characteristics, behaviour patterns, etc. This thesis focuses on enhancing web log based recommendation by personalized retrieval, and its main works and innovations include:
• For personalized retrieval, the thesis proposes two models to improve user experience and optimize search performance. The first is a query suggestion model based on query semantics and click-through data. This model calculates the subject relevance between queries, and then combines the semantic information and the relevance of the query-click matrix model as this can effectively eliminate the ambiguity and input errors of reminder queries. The second is a collaborative filtering retrieval model based on local and global features. By the integration of the local and global characteristics of the accessed information, this model overcomes the limitations of a single feature, and increases the degree of application of the retrieval model.
• For recommendation by personalized retrieval, we propose two recommendation models based on the web log. The first is based on the user’s atomic retrieval transaction sequence and the browse characteristics. This model decomposes search transactions, and calculates the user’s degree of interest on the search term, which allows users to query information more clearly. Further, it incorporates the user feedback on the search results evaluation value, which overcomes the shortcomings of the model based on content filtering. The second model is based on user interests association findings, which can be used to: find the relationship between resources accessed by users, extract the associations of user interests, and address the problem of user interests isolation
Assessing the Effectiveness and Usability of Personalized Internet Search through a Longitudinal Evaluation
This paper discusses a longitudinal user evaluation of Prospector, a personalized Internet meta-search engine capable of personalized re-ranking of search results. Twenty-one participants used Prospector as their primary search engine for 12 days, agreed to have their interaction with the system logged, and completed three questionnaires. The data logs show that the personalization provided by Prospector is successful: participants preferred re-ranked results that appeared higher up. However, the questionnaire results indicated that people would prefer to use Google instead (their search engine of choice). Users would, nevertheless, consider employing a personalized search engine to perform searches with terms that require disambiguation and/or contextualization. We conclude the paper with a discussion on the merit of combining system- and user-centered evaluation for the case of personalized systems
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
The Partial Evaluation Approach to Information Personalization
Information personalization refers to the automatic adjustment of information
content, structure, and presentation tailored to an individual user. By
reducing information overload and customizing information access,
personalization systems have emerged as an important segment of the Internet
economy. This paper presents a systematic modeling methodology - PIPE
(`Personalization is Partial Evaluation') - for personalization.
Personalization systems are designed and implemented in PIPE by modeling an
information-seeking interaction in a programmatic representation. The
representation supports the description of information-seeking activities as
partial information and their subsequent realization by partial evaluation, a
technique for specializing programs. We describe the modeling methodology at a
conceptual level and outline representational choices. We present two
application case studies that use PIPE for personalizing web sites and describe
how PIPE suggests a novel evaluation criterion for information system designs.
Finally, we mention several fundamental implications of adopting the PIPE model
for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio
Personalized Emphasis Framing for Persuasive Message Generation
In this paper, we present a study on personalized emphasis framing which can
be used to tailor the content of a message to enhance its appeal to different
individuals. With this framework, we directly model content selection decisions
based on a set of psychologically-motivated domain-independent personal traits
including personality (e.g., extraversion and conscientiousness) and basic
human values (e.g., self-transcendence and hedonism). We also demonstrate how
the analysis results can be used in automated personalized content selection
for persuasive message generation
Approaches for Future Internet architecture design and Quality of Experience (QoE) Control
Researching a Future Internet capable of overcoming the current Internet limitations is a strategic
investment. In this respect, this paper presents some concepts that can contribute to provide some guidelines to
overcome the above-mentioned limitations. In the authors' vision, a key Future Internet target is to allow
applications to transparently, efficiently and flexibly exploit the available network resources with the aim to
match the users' expectations. Such expectations could be expressed in terms of a properly defined Quality of
Experience (QoE). In this respect, this paper provides some approaches for coping with the QoE provision
problem
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