[Paper Review] A semantically enriched web usage based recommendation model

C. Ramesh, K. Venkateswara Rao+1 more|arXiv (Cornell University)|Nov 10, 2011

Recommender Systems and Techniques9 references3 citations

TL;DR

This paper proposes a semantically enriched web usage mining framework that integrates ontology-based semantic modeling with sequential pattern mining to improve web recommendation quality. By extracting frequent navigational patterns as ontology instances instead of raw page views, the model generates smarter, more context-aware recommendations, demonstrating that semantic enrichment enhances pattern relevance and system functionality in personalized web services.

ABSTRACT

With the rapid growth of internet technologies, Web has become a huge repository of information and keeps growing exponentially under no editorial control. However the human capability to read, access and understand Web content remains constant. This motivated researchers to provide Web personalized online services such as Web recommendations to alleviate the information overload problem and provide tailored Web experiences to the Web users. Recent studies show that Web usage mining has emerged as a popular approach in providing Web personalization. However conventional Web usage based recommender systems are limited in their ability to use the domain knowledge of the Web application. The focus is only on Web usage data. As a consequence the quality of the discovered patterns is low. In this paper, we propose a novel framework integrating semantic information in the Web usage mining process. Sequential Pattern Mining technique is applied over the semantic space to discover the frequent sequential patterns. The frequent navigational patterns are extracted in the form of Ontology instances instead of Web page views and the resultant semantic patterns are used for generating Web page recommendations to the user. Experimental results shown are promising and proved that incorporating semantic information into Web usage mining process can provide us with more interesting patterns which consequently make the recommendation system more functional, smarter and comprehensive.

Motivation & Objective

• Address the limitations of conventional web usage mining that rely solely on low-level access logs without domain knowledge.
• Overcome the poor quality of discovered patterns in traditional recommender systems due to lack of semantic context.
• Integrate domain-specific semantic information (via ontologies) into the web usage mining pipeline to improve recommendation relevance.
• Develop a framework that transforms user navigation sequences into semantic patterns for more meaningful recommendations.
• Enhance the comprehensiveness and intelligence of web recommendation systems by leveraging both usage behavior and semantic relationships.

Proposed method

• Model web navigation sequences using an ontology to represent user behavior as semantic instances rather than raw page views.
• Apply sequential pattern mining techniques to the semantic space to discover frequent navigational patterns.
• Represent user access patterns as instances of domain-specific ontologies, preserving semantic context and relationships.
• Use the resulting semantic patterns to generate personalized web page recommendations based on user behavior and domain knowledge.
• Construct a knowledge base of semantic navigational patterns from usage logs, enabling richer inference and recommendation logic.
• Integrate the semantic model with a recommendation engine that prioritizes semantically coherent and contextually relevant content.

Experimental results

Research questions

• RQ1 How can semantic information be effectively integrated into web usage mining to improve recommendation quality?
• RQ2 What are the characteristics of frequent navigational patterns when represented in a semantic space rather than as raw page views?
• RQ3 To what extent does incorporating domain knowledge via ontologies enhance the relevance and comprehensiveness of web recommendations?
• RQ4 Can semantic enrichment lead to more meaningful and functional recommendation patterns compared to traditional usage-based approaches?
• RQ5 How does the use of ontology instances improve the interpretability and performance of web recommendation systems?

Key findings

• The integration of semantic information into web usage mining leads to more interesting and contextually relevant navigational patterns.
• Semantic patterns extracted as ontology instances provide a richer representation of user behavior than traditional page-view-based sequences.
• The proposed model generates more functional and comprehensive recommendations by leveraging domain knowledge and semantic relationships.
• Experimental results demonstrate that semantic enrichment improves the quality and relevance of discovered patterns in web usage mining.
• The framework shows promise in reducing information overload by delivering smarter, more personalized web experiences based on semantic context.
• The use of ontologies enables better generalization and inference in recommendation systems, moving beyond simple co-visitation frequencies.