Introduction to intelligent techniques for Web personalization

Abstract

Web personalization [Anand and Mobasher 2005] can be defined as any set of actions that can tailor the Web experience to a particular user or set of users. The experience can be something as casual as browsing a Web site or as (economically) significant as trading stocks or purchasing a car. The actions can range from simply making the presentation more pleasing to anticipating the needs of a user and providing customized and relevant information. To achieve effective personalization, organizations must rely on all available data, including the usage and click-stream data (reflecting user behavior), site content, site structure, and domain knowledge, as well as user demographics and profiles. Efficient and intelligent techniques are needed to mine this data for actionable knowledge, and to effectively use the discovered knowledge to enhance the users' Web experience.Being data driven; Web personalization is also achieved through the implementation of all the phases of a typical data mining cycle [Mobasher 2007] including data collection, preprocessing, pattern discovery and evaluation, in an off-line mode, and finally the deployment of the knowledge in realtime to mediate between the user and the Web. The data collection and preprocessing phases involve intelligently extracting and integrating useful data from multiple sources and extracting user preferences implicit within the data. The pattern discovery phase usually involves the adaptation and integration of techniques from machine learning, information retrieval and filtering, databases, agent architectures, knowledge representation, data mining, text mining, statistics, information security and privacy, and context modeling with the goal of building single or group user models. These techniques must address Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications important challenges emanating from the size of the data and the fact that they are heterogeneous and very personal in nature, as well as the dynamic nature of user interactions with the Web. Evaluation of the user models learned from the data involves the estimation of the accuracy of the models for predicting content that may be interesting to a user as well as other aspects such as explainability of the recommendations, diversity of the recommendation set, serendipity of the recommendations, and user satisfaction [Herlocker et al. 2004].Personalization systems depend on access to user profiles...

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