Personalization and usage data in academic libraries : an exploratory study

Personalization is a service pattern for ensuring proactive information delivery tailored to an individual based on learned or perceived needs of the person. It is credited as a remedy for information explosion especially in the academic environment and its importance to libraries was described to the extent of justifying their existence. There have been numerous novel approaches or technical specifications forwarded for realization of personalization in libraries. However, literature shows that the implementation of the services in libraries is minimal which implies the need for a thorough analysis and discussion of issues underlying the practicality of this service in the library environment. This study was initiated by this need and it was done with the objective of finding answers for questions related to library usage data, user profiles and privacy which are among the factors determining the success of personalized services in academic libraries. With the aim of finding comprehensive answers, five distinct cases representing different approaches to academic library personalization were chosen for thorough analysis and themes extracted from them was substantiated by extensive literature review. Moreover, with the aim of getting more information, unstructured questions were presented to the libraries running the services. The overall finding shows that personalization can be realized in academic libraries but it has to address issues related to collecting and processing user/usage data, user interest management, safeguarding user privacy, library privacy laws and other important matters discovered in the course of the study.Joint Master Degree in Digital Library Learning (DILL

The Influence of Online Product Recommendations on Consumer Choice-Making Confidence, Effort, and Satisfaction

The number of products and services available online is growing at a tremendous pace. Consumers increasingly desire the ability to filter through the noise and quickly discover the products that are most relevant to their needs. Many businesses are implementing product recommender systems to provide this ability to consumers, and the result is often increased sales and more satisfied customers. However, recommender systems can also have negative consequences for consumers. For example, a recommender system can bias consumers to purchase more expensive products. Additionally, theories of consumer choice-making suggest that recommender systems can sometimes make purchase choices more difficult, resulting in outcomes that are contrary to the intended purposes of the system, such as customers expending greater shopping effort and feeling less satisfied as a result of receiving too many suggestions. The purpose of this dissertation is to further explore when recommender systems can negatively affect consumers’ online shopping experiences. I investigate three research questions: 1) When do product recommendations increase, rather than decrease, shopping effort? 2) When do product recommendations decrease, rather than increase, shopping satisfaction? And 3) When do recommender systems decrease, rather than increase, consumers’ choice-making confidence? I propose to study these questions by conducting an experiment using a fictitious retail website and online survey

A COLLABORATIVE FILTERING APPROACH TO PREDICT WEB PAGES OF INTEREST FROMNAVIGATION PATTERNS OF PAST USERS WITHIN AN ACADEMIC WEBSITE

This dissertation is a simulation study of factors and techniques involved in designing hyperlink recommender systems that recommend to users, web pages that past users with similar navigation behaviors found interesting. The methodology involves identification of pertinent factors or techniques, and for each one, addresses the following questions: (a) room for improvement; (b) better approach, if any; and (c) performance characteristics of the technique in environments that hyperlink recommender systems operate in. The following four problems are addressed:Web Page Classification. A new metric (PageRank × Inverse Links-to-Word count ratio) is proposed for classifying web pages as content or navigation, to help in the discovery of user navigation behaviors from web user access logs. Results of a small user study suggest that this metric leads to desirable results.Data Mining. A new apriori algorithm for mining association rules from large databases is proposed. The new algorithm addresses the problem of scaling of the classical apriori algorithm by eliminating an expensive joinstep, and applying the apriori property to every row of the database. In this study, association rules show the correlation relationships between user navigation behaviors and web pages they find interesting. The new algorithm has better space complexity than the classical one, and better time efficiency under some conditionsand comparable time efficiency under other conditions.Prediction Models for User Interests. We demonstrate that association rules that show the correlation relationships between user navigation patterns and web pages they find interesting can be transformed intocollaborative filtering data. We investigate collaborative filtering prediction models based on two approaches for computing prediction scores: using simple averages and weighted averages. Our findings suggest that theweighted averages scheme more accurately computes predictions of user interests than the simple averages scheme does.Clustering. Clustering techniques are frequently applied in the design of personalization systems. We studied the performance of the CLARANS clustering algorithm in high dimensional space in relation to the PAM and CLARA clustering algorithms. While CLARA had the best time performance, CLARANS resulted in clusterswith the lowest intra-cluster dissimilarities, and so was most effective in this regard

Personalized Recommendations Based On Users’ Information-Centered Social Networks

The overwhelming amount of information available today makes it difficult for users to find useful information and as the solution to this information glut problem, recommendation technologies emerged. Among the several streams of related research, one important evolution in technology is to generate recommendations based on users’ own social networks. The idea to take advantage of users’ social networks as a foundation for their personalized recommendations evolved from an Internet trend that is too important to neglect – the explosive growth of online social networks. In spite of the widely available and diversified assortment of online social networks, most recent social network-based recommendations have concentrated on limited kinds of online sociality (i.e., trust-based networks and online friendships). Thus, this study tried to prove the expandability of social network-based recommendations to more diverse and less focused social networks. The online social networks considered in this dissertation include: 1) a watching network, 2) a group membership, and 3) an academic collaboration network. Specifically, this dissertation aims to check the value of users’ various online social connections as information sources and to explore how to include them as a foundation for personalized recommendations. In our results, users in online social networks shared similar interests with their social partners. An in-depth analysis about the shared interests indicated that online social networks have significant value as a useful information source. Through the recommendations generated by the preferences of social connection, the feasibility of users’ social connections as a useful information source was also investigated comprehensively. The social network-based recommendations produced as good as, or sometimes better, suggestions than traditional collaborative filtering recommendations. Social network-based recommendations were also a good solution for the cold-start user problem. Therefore, in order for cold-start users to receive reasonably good recommendations, it is more effective to be socially associated with other users, rather than collecting a few more items. To conclude, this study demonstrates the viability of multiple social networks as a means for gathering useful information and addresses how different social networks of a novelty value can improve upon conventional personalization technology

Tag based Bayesian latent class models for movies : economic theory reaches out to big data science

For the past 50 years, cultural economics has developed as an independent research specialism. At its core are the creative industries and the peculiar economics associated with them, central to which is a tension that arises from the notion that creative goods need to be experienced before an assessment can be made about the utility they deliver to the consumer. In this they differ from the standard private good that forms the basis of demand theory in economic textbooks, in which utility is known ex ante. Furthermore, creative goods are typically complex in composition and subject to heterogeneous and shifting consumer preferences. In response to this, models of linear optimization, rational addiction and Bayesian learning have been applied to better understand consumer decision- making, belief formation and revision. While valuable, these approaches do not lend themselves to forming verifiable hypothesis for the critical reason that they by-pass an essential aspect of creative products: namely, that of novelty. In contrast, computer sciences, and more specifically recommender theory, embrace creative products as a study object. Being items of online transactions, users of creative products share opinions on a massive scale and in doing so generate a flow of data driven research. Not limited by the multiple assumptions made in economic theory, data analysts deal with this type of commodity in a less constrained way, incorporating the variety of item characteristics, as well as their co-use by agents. They apply statistical techniques supporting big data, such as clustering, latent class analysis or singular value decomposition. This thesis is drawn from both disciplines, comparing models, methods and data sets. Based upon movie consumption, the work contrasts bottom-up versus top-down approaches, individual versus collective data, distance measures versus the utility-based comparisons. Rooted in Bayesian latent class models, a synthesis is formed, supported by the random utility theory and recommender algorithm methods. The Bayesian approach makes explicit the experience good nature of creative goods by formulating the prior uncertainty of users towards both movie features and preferences. The latent class method, thus, infers the heterogeneous aspect of preferences, while its dynamic variant- the latent Markov model - gets around one of the main paradoxes in studying creative products: how to analyse taste dynamics when confronted with a good that is novel at each decision point. Generated by mainly movie-user-rating and movie-user-tag triplets, collected from the Movielens recommender system and made available as open data for research by the GroupLens research team, this study of preference patterns formation for creative goods is drawn from individual level data

Impression-Aware Recommender Systems

Novel data sources bring new opportunities to improve the quality of recommender systems. Impressions are a novel data source containing past recommendations (shown items) and traditional interactions. Researchers may use impressions to refine user preferences and overcome the current limitations in recommender systems research. The relevance and interest of impressions have increased over the years; hence, the need for a review of relevant work on this type of recommenders. We present a systematic literature review on recommender systems using impressions, focusing on three fundamental angles in research: recommenders, datasets, and evaluation methodologies. We provide three categorizations of papers describing recommenders using impressions, present each reviewed paper in detail, describe datasets with impressions, and analyze the existing evaluation methodologies. Lastly, we present open questions and future directions of interest, highlighting aspects missing in the literature that can be addressed in future works.Comment: 34 pages, 103 references, 6 tables, 2 figures, ACM UNDER REVIE

Mining Users’ Preference Similarities in E-commerce Systems Based on Webpage Navigation Logs

Mining users’ preference patterns in e-commerce systems is a fertile area for a great many application directions, such as shopping intention analysis, prediction and personalized recommendation. The web page navigation logs contain much potentially useful information, and provide opportunities for understanding the correlation between users’ browsing patterns and what they want to buy. In this article, we propose a web browsing history mining based user preference discovery method for e-commerce systems. First of all, a user-browsing-history-hierarchical-presentationgraph to established to model the web browsing histories of an individual in common e-commerce systems, and secondly an interested web page detection algorithm is designed to extract users’ preference. Finally, a new method called UPSAWBH (User Preference Similarity Calculation Algorithm Based on Web Browsing History), which measure the level of users’ preference similarity on the basis of their web page click patterns, is put forward. In the proposed UPSAWBH, we take two factors into account: 1) the number of shared web page click sequence, and 2) the property of the clicked web page that reflects users’ shopping preference in e-commerce systems. We conduct experiments on real dataset, which is extracted from the server of our self-developed e-commerce system. The results indicate a good effectiveness of the proposed approach