Utilizing Search Intent in Topic Ontology-Based User Profile for Web Mining

Zhou, Xujuan; Wu, Sheng‐Tang; Li, Y.; Xu, Yan; Lau, Raymond Y. K.; Bruza, Peter

doi:10.1109/wi.2006.186

Cited by 29 publications

(15 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…User profile is generated from users' interested topics (i.e.,) search intent. Similarity amid concepts is represented through extent to which they distribute information (Zhou et al, 2006a;2006b).…”

Section: Topic Ontology Constructionmentioning

confidence: 99%

“…T is called a group of primitive objects. The concepts are constructed from the primitive objects and ontology contains the primitive and compound classes and these are inherited by resultant classes (Zhou et al, 2006a;Li and Zhong, 2004). Keywords k1 and k2 score function f through a relative r, depends on an Association Score (AS) between keywords and relation weight.…”

Section: Topic Ontology Constructionmentioning

confidence: 99%

“…Association in the top of ontology is characterized by an association group <support, β> from T such that β(S) = {(k1, w1), (k2, w2),…, (kn, wn)} and β(S ) is S's usual type. Association group charts a prototype to a keyword set and presents keyword weight for keywords in a keyword set (Zhou et al, 2006a). Some patterns are discovered from relevant documents that are corresponding to a group of keyword occurrence couples: d1 = {(java language, 4), (programming, 6)} d2 = {(c++ language, 5), (programming, 15)} d3 = {(OOPS, 3), (programming, 7), (others, 10)} Compound objects are obtained using is-a relationship e1 and 2 from d1-3, where d1→e1, d2→e1 and d3→e2.…”

Section: Topic Ontology Constructionmentioning

confidence: 99%

See 2 more Smart Citations

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

Tim¹

2012

Journal of Computer Science

View full text Add to dashboard Cite

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.

show abstract

Section: Topic Ontology Constructionmentioning

confidence: 99%

Section: Topic Ontology Constructionmentioning

confidence: 99%

Section: Topic Ontology Constructionmentioning

confidence: 99%

See 1 more Smart Citation

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

Tim¹

2012

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…Nowadays, given their powerful knowledge representation formalism and associated inference mechanisms, ontologies are emerging as a natural choice for the next-generation user profiles [6]. Ontology-based user profile representations [7][8][9] vary in ontology type, purpose, domain and way the ontologies are used. The main advantages of ontology-based user profiles are: (a) they favor the interpretation of the information gathered in the user profile, (b) they facilitate the exchange and reutilization of user profiles among different systems, (c) they overcome the current syntactic and structural differences among different proposals of user modeling and (d) they structure the domain knowledge of the application in which the agent assists the user.…”

Section: Related Work In User Profile Representationsmentioning

confidence: 99%

Ontology-based user profile learning

Eyharabide

Amandi

2011

Appl Intell

View full text Add to dashboard Cite

Personal agents gather information about users in a user profile. In this work, we propose a novel ontologybased user profile learning. Particularly, we aim to learn context-enriched user profiles using data mining techniques and ontologies. We are interested in knowing to what extent data mining techniques can be used for user profile generation, and how to utilize ontologies for user profile improvement. The objective is to semantically enrich a user profile with contextual information by using association rules, Bayesian networks and ontologies in order to improve agent performance. At runtime, we learn which the relevant contexts to the user are based on the user's behavior observation. Then, we represent the relevant contexts learnt as ontology segments. The encouraging experimental results show the usefulness of including semantics into a user profile as well as the advantages of integrating agents and data mining using ontologies.

show abstract

“…Initially, a set of profiles has to be established, either based on grouping of the most frequent characteristics inside a single service (e.g., Internet browsing or IPTV viewing) or applying advanced methods such as discussed in [24] for ontology-based initial user profiles using a patterntaxonomy model in order to identify search intentions.…”

Section: Ad Click Pattern Matchingmentioning

confidence: 99%