Presentation on theme: "22 nd User Modeling, Adaptation and Personalization (UMAP 2014) Time-Sensitive User Profile for Optimizing Search Personalization Ameni Kacem, Mohand Boughanem,"— Presentation transcript:
22 nd User Modeling, Adaptation and Personalization (UMAP 2014) Time-Sensitive User Profile for Optimizing Search Personalization Ameni Kacem, Mohand Boughanem, Rim Faiz
Introduction Related Work Time-Based User Profile Conclusion Experiments and Results Outline
Context (1) 4 Personalization: search results adapted to the user’s information needs and inetrests. Time integration when the words appear Time to discern short- term and long –term uer profiles
Context (2) Interactions extracted from the current sesssion No long-term interests Short-term Old interests No consideration of the actual user needs Long-term 5
Problem Description (1) Weight the profiles terms: both the freshness and the frequency Unify both the recent and persistent interests. 6 User interests evolution over time A time sensitive user profile (older frequent terms should not outperform current and not frequent terms).
Problem Description (2) 7 How do temporal dynamics affect the quality of user models in the context of personalized search ? How short-term (recent) profile and long- term (persistent) profile interact ? How each of the profiles may be used in separation or unified ?
User Profiling User profiling Information about the user from different sources Multiple representations: 9 Vector Weighted keywords Categories Open Directory Project Semantic network Concepts
Short-term user profile Short-term: the interests and needs of users related to activities of the current search session. Daoud et al., 2009; Zemirli, 2008: the short-term user profile is all the interactions and interests related to a single information need. Dumais et al., 2003; Shen et al. 1999: it represents multiple interests emerged in a single time slot. 10
Long-term user profile Long-term: The use of specific information: education level, general interests, user query history and past user clickthrough information. Teevan et al. (2005): rich long-term user models based on desktop search activities to improve ranking. Tan et al. (2006): long-term language model-based representations of users’ interests based on queries, documents and clicks. 11
Recent Similar Works 12 PaperApproach Bennett et al. (2012) The first study to assess how short-term and long-term behaviors relate, and how each may be used in isolation or in combination. Abel et al. (2013) Different strategies for mining user interest profiles from microblogging activities ranging such as strategies that adapt to temporal patterns that can be observed in the microblogging behavior.
Temporal User Profile The user profile can reflect both the recurrent (persistent) and the current (recent) interests but with different scales based on freshness. 13 Users who are not very active The short-term profile can eliminate relevant results which are more related to their personal interests. For users who are very active The aggregation of recent activities without ignoring the old interests would be very interesting.
Proposed Approach The user profile: a vector of keywords terms corresponding to the user interests implicitly inferred from his activities on social Web systems. Adjust the importance of each keyword according to the time of its use. Unified model: Naturally combine short-term profile and long-term profile into a single. Give importance to the recent interests without ignoring the continuous ones. 15
Main idea Personalization in this work: weighting the user profile keywords according to the appearing time in addition to the frequency. Main idea Revising the notion of frequency by adjusting it with a temporal function Ensure a unified profile 16
Time-Sensitive User Profile 18
Time-Sensitive User Profile 19
Experiments and Results
Data Set of december, Profiles Tweets 40 assessors
Methodology 1. Create the user profile Extract the user tweets Combine the relative frequencies with the temporal biased function 2. Submit a query to standard search engine Related to the user’s areas of interests defined on Twitter (800 queries) 3. Results Extraction Top 100 Webpages 22
Methodology Stop words removal, stemming and tokenization of documents and users’ extracted terms (Apache Lucene classes, Porter Stemming Filter). 3. Create the Webpage-profile Weight according to the tf-idf model 4. Rerank search results 23
Impact of User's Profile Information Amount Influence of the temporal feature: same personalization strategy to compare the time-sensitive user profile (TSUP) with the nTF-based user profile. 25 Profile Temporal Aspects Short-term Long-term Single
Impact of User's Profile Information Amount 26
Findings Promising values: Term frequency does not reflect the freshness of an interest but gives an overview of how often the user mentioned a term. Standard search engines return relevant results to the user query’s terms but they are indifferent to the users’ interests 27 Temporal function: consider the actual interests which are used to enhance the current search without overlooking the persistent interests and helps to personalize recurrent information needs.
Problem of personalized search: a user-modeling framework for Twitter microblogging system. Integration of the social data: accurate and efficient because people are likely to write a blog or bookmark a Webpage about something that interests them. 29 How the temporal-based user profile influences the accuracy of personalized seach using a single profile instead of separately consider the short- and long-term user profiles?
Conclusion Vector-based representation Temporal- Frequency Merging the term frequency and the freshness of each keyword (Kernel Function) 30 Encouraging results: comparison to two non-temporal sensitive approaches. Aggregation of the current and recurrent interests: increasing amount of information yields to better improvement.
Future Work Improve experiments: study temporal aspects when enriching the user profile by including diverse user’s social behaviors on the Web. Comparison with other temporal models 31
Thank You For Your Attention AMENI KACEM PHD STUDENT PAUL SABATIER UNIVERSITY FRANCE HIGH INSTITUTE OF MANAGEMENT,TUNISIA