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16 ReferencesPreference Mining: A Novel Approach on Mining User Preferences for Personalized Applications
Abstract
Advanced personalized e-applications require comprehensive knowl- edge about their user's likes and dislikes in order to provide individual product recommendations, personal customer advice and custom-tailored product of- fers. In our approach we model such preferences as strict partial orders with "A is better than B" semantics, which has been proven to be very suitable in vari- ous e-applications. In this paper we present novel Preference Mining techniques for detecting strict partial order preferences in user log data. The main advan- tage of our approach is the semantic expressiveness of the Preference Mining results. Experimental evaluations prove the effectiveness and efficiency of our algorithms. Since the Preference Mining implementation uses sophisticated SQL statements to execute all data-intensive operations on database layer, our algorithms scale well even for large log data sets. With our approach personal- ized e-applications can gain valuable knowledge about their customers' prefer- ences, which is essential for a qualified customer service.
- CitationsCitations88
- ReferencesReferences16
- User preferences for software features can be gathered in different ways [49], [24], [16], [45] depending on the nature of a software release. For the very first release of a software, users' preferences could be gathered by conventional market research approaches such as conducting surveys or referring to the users' feedbacks or sales records of the similar software products in the market.
[Show abstract] [Hide abstract] ABSTRACT: Considering user preferences is a determining factor in optimizing the value of a software release. This is due to the fact that user preferences for software features specify the values of those features and consequently determine the value of the release. Certain features of a software however, may encourage or discourage users to prefer (select or use) other features. As such, value of a software feature could be positively or negatively influenced by other features. Such influences are known as Value-related Feature (Requirement) Dependencies. Value-related dependencies need to be considered in software release planning as they influence the value of the optimal subset of the features selected by the release planning models. Hence, we have proposed considering value-related feature dependencies in software release planning through mining user preferences for software features. We have demonstrated the validity and practicality of the proposed approach by studying a real world software project.- Preference Learning (PL) is aiming to learn a predictive preference model from observations (empirical data) that reveal, explicitly or implicitly, information about the specific preferences of a user or a group of users. This can be supported by methods for preference mining, e.g., to gain knowledge about users likes and dislikes to provide personal and custom-tailored recommendations[55]. PL can be seen as a natural link between ML and decision support, and was primarily applied in information retrieval with the central task of learning to rank[56,57].
[Show abstract] [Hide abstract] ABSTRACT: Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.- From these rules, an order is inferred on the tuples. The underlying preference model is the Pareto preference model as in [7]. In this model, preferences are non-contextual, that is, preferences on values of attributes do not depend on the values of other attributes (the context).
[Show abstract] [Hide abstract] ABSTRACT: The emerging of ubiquitous computing technologies in recent years has given rise to a new field of research consisting in incorporating context-aware preference querying facilities in database systems. One important step in this setting is the Preference Elicitation task which consists in providing the user ways to inform his/her choice on pairs of objects with a minimal effort. In this paper we propose an automatic preference elicitation method based on mining techniques. The method consists in extracting a user profile from a set of user preference samples. In our setting, a profile is specified by a set of contextual preference rules verifying properties of soundness and conciseness. After proving that the problem is NP-complete, we propose a resolution in 2 phases. The first phase extracts all individual user preferences by means of contextual preference rules. The second phase builds the user profile starting from this collection of rules using a greedy method. To assess the quality of user profiles, we propose three ranking techniques benefiting from these profiles that enable us to rank objects according to user preferences. We evaluate the efficacy of our three ranking strategies and compare them with a well-known ranking method (SVMRank). The evaluation is carried out through an extensive set of experiments executed on a real-world database of user preferences about movies.- It is now admitted in the database community that query logs can be leveraged not only for physical tuning, but also for user empowerment [14]. Logs can indeed be used for learning user preferences [12], for query recommendation [7, 21], for query auto-completion [15], or for query composition [16] . In particular , the study of [16] showed that SQL query composition time can be heavily reduced when users are provided with a tool to browse, search and reuse former SQL queries organized in sessions.
[Show abstract] [Hide abstract] ABSTRACT: OLAP is the main paradigm for flexible and effective exploration of multidimensional cubes in data warehouses. During an OLAP session the user analyzes the results of a query and determines a new query that will give her a better understanding of information. Given the huge size of the data space, this exploration process is often tedious and may leave the user disoriented and frustrated. This paper presents an OLAP tool named Falseto (Former AnalyticaL Sessions for lEss Tedious Olap), that is meant to assist query and session composition, by letting the user summarize, browse, query, and reuse former analytical sessions. Falseto's implementation on top of a formal framework is detailed. We also report the experiments we run to obtain and analyze real OLAP sessions and assess Falseto with them. Finally, we discuss how Falseto can be seen as a starting point for bridging OLAP with exploratory search, a search paradigm centered on the user and the evolution of her knowledge.- A preference mining algorithm aims at extracting an accurate preference model from a sample dataset of preferences provided by the user. The formalisms commonly used for preference representation in the preference mining techniques found in the literature are score functions [2, 5, 6] (the user provides a rating for each object in the sample dataset), pairwise comparison of alternatives [1,7,9,10,12] (for each pair of objects in the sample database, the user informs which one he/she prefers) and ranking of alternatives [8, 11] (the user provides a sequence of objets in decreasing order of preference). Classifiers versus Preference Mining Techniques.
[Show abstract] [Hide abstract] ABSTRACT: Recent research work on preference mining has focused on the development of methods for mining a preference model from prefer- ence data following a crisp pairwise representation. In this representa- tion, the user has two options regarding a pair of objects u and v: either he/she prefers u to v or v to u. In this article, we propose FuzzyPrefMiner, a method for extracting fuzzy contextual preference models from fuzzy preference data characterized by the fact that, given two objects u; v the user has a spectrum of options according to his degree of preference on u and v. Accordingly, the mined preference model is fuzzy, in the sense that it is capable to predict, given two new objects u and v, the de- gree of preference the user would assign to these objects. The e�ciency of FuzzyPrefMiner is analysed through a series of experiments on real datasets.- From these rules, an order is inferred on the tuples. The underlying preference model is the pareto preference model as in [8]. Recommender Systems.
[Show abstract] [Hide abstract] ABSTRACT: For nowadays users, the tasks of searching and chosing for items in the web that most fulfill their expectations have becoming more and more overwhelming due to the huge amount of information available. Recommender Systems (RS) emerged as indispensable tools in this information overload scenario in order to filtering out what could be potentially interesting to the user. Several techniques for building RS have been proposed so far, based on different strategies. Data Mining techniques have successfully been used in RS research in order to mitigate the well-known item cold-start challenge. However, the mining techniques used are generally classical classification algorithms used to predict user evaluations on individual items. In this article, we present PrefRec, a general framework for developing RS using Preference Mining and Pref- erence Aggregation techniques. We focus on Pairwise Preference Mining techniques allowing to predict which, between two objects, is the preferred one. These specific techniques have far better performance than algorithms originally designed for classification tasks. A preliminary empirical study for analyzing the influence of the different factors involved in each of the five modules of PrefRec is presented.
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