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16
A Survey on Representation, Composition and Application of Preferences in Database Systems
- ACM TODS
, 2011
"... Preferences have been traditionally studied in philosophy, psychology, and economics and applied to decision making problems. Recently, they have attracted the attention of researchers in other fields, such as databases where they capture soft criteria for queries. Databases bring a whole fresh pers ..."
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Cited by 30 (6 self)
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Preferences have been traditionally studied in philosophy, psychology, and economics and applied to decision making problems. Recently, they have attracted the attention of researchers in other fields, such as databases where they capture soft criteria for queries. Databases bring a whole fresh perspective to the study of preferences, both computational and representational. From a representational perspective, the central question is how we can effectively represent preferences and incorporate them in database querying. From a computational perspective, we can look at how we can efficiently process preferences in the context of database queries. Several approaches have been proposed but a systematic study of these works is missing. The purpose of this survey is to provide a framework for placing existing works in perspective and highlight critical open challenges to serve as a springboard for researchers in database systems. We organize our study around three axes: preference representation, preference composition, and preference query processing.
Toward Context and Preference-Aware Location-based Services
, 2009
"... The explosive growth of location-detection devices, wireless communications, and mobile databases has resulted in the realization of location-based services as commercial products and research prototypes. Unfortunately, current locationbased applications (e.g., store finders) are rigid as they are c ..."
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Cited by 14 (1 self)
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The explosive growth of location-detection devices, wireless communications, and mobile databases has resulted in the realization of location-based services as commercial products and research prototypes. Unfortunately, current locationbased applications (e.g., store finders) are rigid as they are completely isolated from various concepts of user “preferences” and/or “context”. Such rigidness results in nonsuitable services (e.g., a vegetarian user may get a restaurant with non-vegetarian menu). In this paper, we introduce the system architecture of a Context and Preference-Aware Location-based Database Server (CareDB, for short), currently under development at University of Minnesota, that delivers personalized services to its customers based on the surrounding context. CareDB goes beyond the traditional scheme of “one size fits all ” of existing location-aware database systems. Instead, CareDB tailors its functionalities and services based on the preference and context of each customer. Examples of services provided by CareDB include a restaurant finder application in which CareDB does not base its choice of restaurants solely on the user location. Instead, CareDB will base its choice on both the user location and surrounding context (e.g., user dietary restriction, user preferences, and road traffic conditions). Within the framework of CareDB, we discuss research challenges and directions towards an efficient and practical realization of context-aware location-based query processing. Namely, we discuss the challenges for designing user profiles, multiobjective query processing, context-aware query optimizers, context-aware query operators, and continuous queries.
Personalizing Queries based on Networks of Composite Preferences
- ACM TRANS. DATABASE SYST
, 2010
"... People's preferences are expressed at varying levels of granularity and detail as a result of partial or imperfect knowledge. One may have some preference for a general class of entities, e.g., liking comedies, and another one for a fine-grained, specific class, e.g., disliking recent thrillers ..."
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Cited by 6 (3 self)
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People's preferences are expressed at varying levels of granularity and detail as a result of partial or imperfect knowledge. One may have some preference for a general class of entities, e.g., liking comedies, and another one for a fine-grained, specific class, e.g., disliking recent thrillers with Al Pacino that are intended for families. In this paper, we are interested in capturing such complex, multi-granular preferences to personalize database queries and in studying their impact on query results. In particular, we organize the collection of one's preferences in a preference network (a directed acyclic graph), where each node refers to a subclass of the entities that its parent refers to, and whenever they both apply, more speci c preferences override more generic ones. We study query personalization based on networks of preferences and provide efficient algorithms for identifying relevant preferences, modifying queries accordingly, and processing these queries to obtain personalized answers. Finally, we present results of both synthetic and realuser experiments, which (a) demonstrate the efficiency of our algorithms, (b) provide insight as to the appropriateness of the proposed preference model and (c) show the benefits of query personalization based on composite preferences compared to simpler preference representations.
One Size Does Not Fit All: Towards User & Query Dependent Ranking For Web Databases
, 2009
"... In this paper, we propose an automated solution for ranking query results of Web databases in an user- and query-dependent environment. We first propose a learning method for inferring a workload of ranking functions by investigating users ’ browsing choices over individual query results. Based on t ..."
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Cited by 5 (1 self)
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In this paper, we propose an automated solution for ranking query results of Web databases in an user- and query-dependent environment. We first propose a learning method for inferring a workload of ranking functions by investigating users ’ browsing choices over individual query results. Based on this workload, we propose a similarity model, based on two novel metrics – user- and query-similarity, for ranking query results when user browsing choices are not available. We present the results of an experimental study that validates our proposal for user-and query-dependent ranking.
Intuitive Network Applications: Learning for Personalized Converged Services Involving Social Networks
"... Abstract — The convergence of the wireline telecom, wireless telecom, and internet networks and the services they provide offers tremendous opportunities in services personalization. We distinguish between two broad categories of personalization systems: recommendation systems, such as used in adver ..."
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Cited by 2 (1 self)
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Abstract — The convergence of the wireline telecom, wireless telecom, and internet networks and the services they provide offers tremendous opportunities in services personalization. We distinguish between two broad categories of personalization systems: recommendation systems, such as used in advertising, and life-style assisting systems, which attempt to customize or specialize services to an individual’s needs, preferences, and habits. The Privacy-Conscious Personalization (PCP) framework, developed previously at Bell Labs, uses a high-speed rules engine to enable rich life-style assisting personalization. During network-hosted information sharing and call processing, the PCP framework can be used to interpret a combination of incoming requests, user data, and user preferences in order to provide contextaware, requester-targeted, and preferences-driven responses to those requests (e.g., deciding whether to share a user’s location with a given requester, what to show as the enduser’s availability to a given requester, where to forward an incoming call). This paper describes key aspects of a new initiative at Bell Labs, called Intuitive Network Applications (INA), which aims to combine human factors and automated learning techniques, in order to gather the user data and preferences needed for PCP-enabled personalization, with minimal disruption to the user. A particular focus of the paper is on life-style assisting capabilities for applications that involve the interaction of an end-user with her social network, i.e., family, friends, colleagues, customers, etc. The paper describes (i) key requirements, (ii) a high-level architectural framework, and (iii) some specific directions currently under exploration for filling out the framework. Index Terms — context, converged services, learning, personalization, preferences, ubiquitous computing I.
Contextual Database Preferences
"... As both the volume of data and the diversity of users accessing them increase, user preferences offer a useful means towards improving the relevance of the query results to the information needs of the specific user posing the query. In this article, we focus on enhancing preferences with context. C ..."
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Cited by 1 (1 self)
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As both the volume of data and the diversity of users accessing them increase, user preferences offer a useful means towards improving the relevance of the query results to the information needs of the specific user posing the query. In this article, we focus on enhancing preferences with context. Context may express conditions on situations external to the database or related to the data stored in the database. We outline models for expressing both types of preferences. Then, given a user query and its surrounding context, we consider the problem of selecting related preferences to personalize the query. 1
A framework for learning to personalize converged services involving social networks
- in Proc. IEEE Workshop on Adaptive and Learning System
, 2006
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Query Personalization based on User Preferences
"... Abstract. Query Personalization is the process of dynamically enhancing a query with related user preferences stored in a user profile with the aim of providing personalized answers. The underlying idea is that different users may find different things relevant to a search due to different preferenc ..."
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Abstract. Query Personalization is the process of dynamically enhancing a query with related user preferences stored in a user profile with the aim of providing personalized answers. The underlying idea is that different users may find different things relevant to a search due to different preferences. Essential ingredients of query personalization are: (a) a model for representing and storing preferences in user profiles, and (b) algorithms for the generation of personalized answers using stored preferences. 1.
Employee Searching based on User and Query- Dependent Ranking
"... The growth of the Web and the Internet leads to the development of an ever increasing number of interesting application classes. The most common method used now in companies is normal recruitment process. If a company wants an employee immediately, the only way for recruitment is advertising in any ..."
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The growth of the Web and the Internet leads to the development of an ever increasing number of interesting application classes. The most common method used now in companies is normal recruitment process. If a company wants an employee immediately, the only way for recruitment is advertising in any media. After receiving applications from the employees, they need to check the qualification, experience etc. It is a time required process. This paper proposes a method for employee searching by using a user and query dependent ranking. Here present a ranking model based on user inputs. This ranking model is acquired from several other ranking functions derived for various user-query pairs. This is based on the intuition that similar users display comparable ranking preferences over the result of similar queries. This paper gives an idea about how the ranking can be used.
