Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis

Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer’s generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers’ generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising

Semantic Web meets Web 2.0 (and vice versa): The Value of the Mundane for the Semantic Web

Web 2.0, not the Semantic Web, has become the face of “the next generation Web” among the tech-literate set, and even among many in the various research communities involved in the Web. Perceptions in these communities of what the Semantic Web is (and who is involved in it) are often misinformed if not misguided. In this paper we identify opportunities for Semantic Web activities to connect with the Web 2.0 community; we explore why this connection is of significant benefit to both groups, and identify how these connections open valuable research opportunities “in the real” for the Semantic Web effort

User Preference Web Search -- Experiments with a System Connecting Web and User

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware supporting top-k answers and a user interface for querying and evaluation of search results. We integrate several tools (implementing our models and methods) into one framework connecting user with the web. The model represents user preferences with fuzzy sets and fuzzy logic, here understood as a scoring describing user satisfaction. This model can be acquired with explicit or implicit methods. Model-theoretic semantics is based on fuzzy description logic f-EL. User preference learning is based on our model of fuzzy inductive logic programming. Our system works both for English and Slovak resources. The primary application domain are job offers and job search, however we show extension to mutual investment funds search and a possibility of extension into other application domains. Our top-k search is optimized with own heuristics and repository with special indexes. Our model was experimentally implemented, the integration was tested and is web accessible. We focus on experiments with several users and measure their satisfaction according to correlation coefficients

Self-tuning Personalized Information Retrieval in an Ontology-Based Framework

Reliability is a well-known concern in the field of personalization technologies. We propose the extension of an ontology-based retrieval system with semantic-based personalization techniques, upon which automatic mechanisms are devised that dynamically gauge the degree of personalization, so as to benefit from adaptivity but yet reduce the risk of obtrusiveness and loss of user control. On the basis of a common domain ontology KB, the personalization framework represents, captures and exploits user preferences to bias search results towards personal user interests. Upon this, the intensity of personalization is automatically increased or decreased according to an assessment of the imprecision contained in user requests and system responses before personalization is applied

Understanding and Mitigating Multi-sided Exposure Bias in Recommender Systems

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be crucial to optimize utilities not just for the end user, but also for other actors such as item sellers or producers who desire a fair representation of their items. Existing solutions do not properly address various aspects of multi-sided fairness in recommendations as they may either solely have one-sided view (i.e. improving the fairness only for one side), or do not appropriately measure the fairness for each actor involved in the system. In this thesis, I aim at first investigating the impact of unfair recommendations on the system and how these unfair recommendations can negatively affect major actors in the system. Then, I seek to propose solutions to tackle the unfairness of recommendations. I propose a rating transformation technique that works as a pre-processing step before building the recommendation model to alleviate the inherent popularity bias in the input data and consequently to mitigate the exposure unfairness for items and suppliers in the recommendation lists. Also, as another solution, I propose a general graph-based solution that works as a post-processing approach after recommendation generation for mitigating the multi-sided exposure bias in the recommendation results. For evaluation, I introduce several metrics for measuring the exposure fairness for items and suppliers, and show that these metrics better capture the fairness properties in the recommendation results. I perform extensive experiments to evaluate the effectiveness of the proposed solutions. The experiments on different publicly-available datasets and comparison with various baselines confirm the superiority of the proposed solutions in improving the exposure fairness for items and suppliers.Comment: Doctoral thesi