An effective approach for personalized web search based on community-cluster analysis

The concept of Personalized Web Search is commonly used for improving the quality of web search results by identifying and facilitating different users' search needs. There are several techniques such as user profiling, content analysis, hyperlink analysis and biased PageRank algorithm that are used to achieve web personalization. User Profiling is one of the widely used techniques for personalizing web search at large scale. But it contains several technical and ethical issues such as privacy violations, inefficient use of computing resources as well. Collaborative web search is also a kind of a relatively "new concept which defines the way of optimizing/personalizing search results by using details of group of people and contributing the knowledge of all of them about web search. This paper presents the details of an alternative approach for personalizing web results by using user profiling technique with community cluster analysis of collaborative web search by adapting concept of reusability 'among web results

The Web 2.0 as Marketing Tool: Opportunities for SMEs

The new generation of Internet applications widely known as Social Media or Web 2.0 offers corporations a whole range of opportunities for improving their marketing efficiency and internal operations. Web 2.0 applications have already become part of the daily life of an increasing number of consumers who regard them as prime channels of communication, information exchange, sharing of expertise, dissemination of individual creativity and entertainment. Web logs, podcasts, online forums and social networks are rapidly becoming major sources of customer information and influence while the effectiveness of traditional mass media is rapidly decreasing. Using the social media as a marketing tool is an issue attracting increasing attention. The hitherto experience is that large public corporations are more likely to make use of such instruments as part of their marketing and internal operations (McKinsey, 2007).The paper defines the Web 2.0 phenomenon and based on the experience of large corporations examines how SMEs could engage the various Web 2.0 instruments in order to efficiently market their products, improve customer relations, increase customer retention and enhance internal operations

Personalizing the design of computer‐based instruction to enhance learning

This paper reports two studies designed to investigate the effect on learning outcomes of matching individuals’ preferred cognitive styles to computer‐based instructional (CBI) material. Study 1 considered the styles individually as Verbalizer, Imager, Wholist and Analytic. Study 2 considered the bi‐dimensional nature of cognitive styles in order to assess the full ramification of cognitive styles on learning: Analytic/Imager, Analytic/ Verbalizer, Wholist/Imager and the Wholist/Verbalizer. The mix of images and text, the nature of the text material, use of advance organizers and proximity of information to facilitate meaningful connections between various pieces of information were some of the considerations in the design of the CBI material. In a quasi‐experimental format, students’ cognitive styles were analysed by Cognitive Style Analysis (CSA) software. On the basis of the CSA result, the system defaulted students to either matched or mismatched CBI material by alternating between the two formats. The instructional material had a learning and a test phase. Learning outcome was tested on recall, labelling, explanation and problem‐solving tasks. Comparison of the matched and mismatched instruction did not indicate significant difference between the groups, but the consistently better performance by the matched group suggests potential for further investigations where the limitations cited in this paper are eliminated. The result did indicate a significant difference between the four cognitive styles with the Wholist/Verbalizer group performing better then all other cognitive styles. Analysing the difference between cognitive styles on individual test tasks indicated significant difference on recall, labelling and explanation, suggesting that certain test tasks may suit certain cognitive styles

Personalized Ranking in eCommerce Search

We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context to augment a generic ranker optimized for conversion and relevance. We use a combination of latent features learned from item co-clicks in historic sessions and content-based features that use item title and price. Personalization in search has been discussed extensively in the existing literature. The novelty of our work is combining and comparing content-based and content-agnostic features and showing that they complement each other to result in a significant improvement of the ranker. Moreover, our technique does not require an explicit re-ranking step, does not rely on learning user profiles from long term search behavior, and does not involve complex modeling of query-item-user features. Our approach captures item co-click propensity using lightweight item embeddings. We experimentally show that our technique significantly outperforms a generic ranker in terms of Mean Reciprocal Rank (MRR). We also provide anecdotal evidence for the semantic similarity captured by the item embeddings on the eBay search engine.Comment: Under Revie