Learning user interests for a session-based personalized search

Daoud, Mariam; Tamine-Lechani, Lynda

doi:10.1145/1414694.1414708

Cited by 35 publications

(21 citation statements)

References 7 publications

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“…This means that concepts are linked by using the taxonomic relationship IS-A. To re-rank the documents according to the user profile, it means that only the concepts that represent the Web pages (for more details see [65]) visited by the user during the previous queries at time t − 1 are taken into account. Then, each selected concept from the ODP (i.e., c j ) is represented as a single term vectorc j , and its similarity score withK t is computed as score(c j ) = cos(c j ,K t .).…”

Section: Approaches Defined To Re-rank the Search Results Based On Thmentioning

confidence: 99%

Ontology-Based Information Behaviour to Improve Web Search

Calegari

Pasi

2010

Future Internet

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Web Search Engines provide a huge number of answers in response to a user query, many of which are not relevant, whereas some of the most relevant ones may not be found. In the literature several approaches have been proposed in order to help a user to find the information relevant to his/her real needs on the Web. To achieve this goal the individual Information Behavior can been analyzed to 'keep' track of the user's interests. Keeping information is a type of Information Behavior, and in several works researchers have referred to it as the study on what people do during a search on the Web. Generally, the user's actions (e.g., how the user moves from one Web page to another, or her/his download of a document, etc.) are recorded in Web logs. This paper reports on research activities which aim to exploit the information extracted from Web logs (or query logs) in personalized user ontologies, with the objective to support the user in the process of discovering Web information relevant to her/his information needs. Personalized ontologies are used to improve the quality of Web search by applying two main techniques: query reformulation and re-ranking of query evaluation results. In this paper we analyze various methodologies presented in the literature aimed at using personalized ontologies, defined on the basis of the observation of Information Behaviour to help the user in finding relevant information.

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Section: Approaches Defined To Re-rank the Search Results Based On Thmentioning

confidence: 99%

Ontology-Based Information Behaviour to Improve Web Search

Calegari

Pasi

2010

Future Internet

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“…Although this method showed an improvement over the original cosine similarity, the accumulation behaviour that it induces puts more emphasis on the top level classes which are too general to represent actual user interests, while middle and low level classes receive less attention. Daoud et al (2008) concur that representing interests with level two of an ontology is too general, while a leaf node representation is too detailed. They suggested that the most relevant concept is the one that has the greater number of dependencies.…”

Section: Stage Three: Mapping Documents To a Reference Ontologymentioning

confidence: 99%

“…The second technique, which was suggested by (Middleton et al, 2004) and (Kim et al, 2007) is adding 50% of each sub-concept's weight to its super-concept, and repeats this process until reaching the root. The third technique is the Sub-class Aggregation Scheme (SAS) that was proposed in (Daoud et al, 2008) where user interests are represented using level three of the ontology. The weight for each level-three concept is computed by adding the weights of its sub-concepts.…”

Section: Evaluation Of the Gew And 3c Algorithmsmentioning

confidence: 99%

Dynamic user profiles for web personalisation

Hawalah

Fasli

2015

Expert Systems with Applications

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Web personalization systems are used to enhance the user experience by providing tailor-made services based on the user's interests and preferences which are typically stored in user profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the users' changing behaviour. In this paper, we introduce a set of methods designed to capture and track user interests and maintain dynamic user profiles within a personalization system. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology and are subsequently used to learn short-term and long-term interests. A multi-agent system facilitates and coordinates the capture, storage, management and adaptation of user interests. We propose a search system that utilizes our dynamic user profile to provide a personalized search experience. We present a series of experiments that show how our system can effectively model a dynamic user profile and is capable of learning and adapting to different user browsing behaviours.

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“…Ranking can be done in same search period based on highest interest scores with topic similarity. Accumulating nodes and edges to user profile allocates topics where user is interested in search session (Daoud et al, 2008).…”

Section: Constructing the User Profile Over A Search Sessionmentioning

confidence: 99%

“…The user profile is embodied using user search record in a search session. User profile is constructed over a search session to personalize search (Daoud et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Ontological Approach for Effective Generation of Concept Based User Profiles to Personalize Search Results

Tim¹

2012

Journal of Computer Science

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Problem statement: Ontological user profile generation was a semantic approach to derive richer concept based user profiles. It depends on the semantic relationship of concepts. This study focuses on ontology to derive concept oriented user profile based on user search queries and clicked documents.This study proposes concept based on topic ontology which derives the concept based user profiles more independently. It was possible to improve the search engine processes more efficiently. Approach: This process consists of individual user's interests, topical categories of user interests and identifies the relationship among the concepts. The proposed approach was based on topic ontology for concept based user profile generation from search engine logs. Spreading activation algorithm was used to optimize the relevance of search engine results. Topic ontology was constructed to identify the user interest by assigning activation values and explore the topics similarity of user preferences. Results: To update and maintain the interest scores, spreading activation algorithm was proposed. User interest may change over the period of time which was reflected to user profiles. According to profile changes, search engine was personalized by assigning interest scores and weight to the topics. Conclusion: Experiments illustrate the efficacy of proposed approach and with the help of topic ontology user preferences can be identified correctly. It improves the quality of the search engine personalization by identifying the user's precise needs.

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Learning user interests for a session-based personalized search

Cited by 35 publications

References 7 publications

Ontology-Based Information Behaviour to Improve Web Search

Ontology-Based Information Behaviour to Improve Web Search

Dynamic user profiles for web personalisation

Ontological Approach for Effective Generation of Concept Based User Profiles to Personalize Search Results

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