This paper analyzes a pick-up pattern of taxi service in Jeju area based on the real-life location history data collected from the Taxi Telematics system, aiming at obtaining useful background data necessary to design a location recommendation service for empty taxis. Out of the great amount of location records, pick-up data are extracted by tracing the state change in the predefined taxi state diagram. To decide a reasonable granularity of location recommendation, refined clustering is performed by means of the well-known k-means method supported by the E-Miner statistics software package. In addition, within each cluster, the temporal analysis creates time-dependent pick-up pattern change along the time axis. As a result, the cluster and its spatio-temporal pick-up frequency make it possible to suggest that the empty taxi go to the nearby cluster location, resulting in the reduction of empty taxi ratio.

Analysis of the Passenger Pick-Up Pattern for Taxi Location Recommendation

Research for the generation of reliable recommendations has been the main goal focused by many researchers in recent years. Though many recommendation approaches have been developed to assist users in the selection of their interesting items in the online world, still the personalization problem exists. In this paper, we present a new recommendation approach to address the problems such as scalability, sparsity, and cold-start in a collective way. We have developed a knowledge-based domain specific ontology for the generation of personalized recommendations. We have also introduced two different ontology-based predictive models as minion representation model and prominent representation model for the effective generation of recommendations to all types of users. The prediction models are induced by data mining algorithms by correlating the user preferences and features of items for user modeling. We have proposed a new variant of KNN algorithm as Adaptive KNN for the collaborative filtering based recommender system. The proposed recommendation approach is validated with standard MovieLens dataset and obtained results are evaluated with Precision, Recall, F-Measure, and Accuracy. The experimental results had proved the better performance of our proposed AKNN algorithm over other algorithms with the highly sparse data taken for the recommendation generation.

Adaptive KNN based Recommender System through Mining of User Preferences

Content adaptation is an important technique for mobile devices. Existing content adaptation systems have been developed with specific adaptation goals. In this paper, we present an extensible content adaptation system, Xadaptor. We take a rule-based approach to facilitate extensible, systematic, and adaptive content adaptation. It integrates adaptation mechanisms for various content types and organizes them into the rule base. Rules are invoked based on the individual client information. We classify HTML page objects into structure, content, and pointer objects. Existing content adaptation techniques mainly focus on content objects and do not consider adaptation for structure and pointer objects. In Xadaptor, novel adaptation techniques for the structure object HTML table have been developed. We use fuzzy logic to model the adaptation quality and guide the adaptation decision. To demonstrate the feasibility of our approach, we have implemented a prototype system. Experimental studies show that Xadaptor is capable of on-the-fly content adaptation and is easily extensible

A Flexible Content Adaptation System Using a Rule-Based Approach

In recent years recommendation systems have changed the way of communication between both websites and users. Recommendation system sorts through massive amounts of data to identify interest of users and makes the information search easier. For that purpose many methods have been used. Collaborative Filtering (CF) is a method of making automatic predictions about the interests of customers by collecting information from number of other customers, for that purpose many collaborative base algorithms are used. CHARM algorithm is one of the frequent patterns finding algorithm which is capable to handle huge dataset, unlike all previous association mining algorithms which do not support huge dataset. This paper covers different techniques which are used in recommendation system and also proposes a new system for efficient web page recommendation based on hybrid collaborative filtering i.e. using collaborative technique and CHARM algorithm which are coupled with the pattern discovery algorithms such as clustering and association rule mining.

Survey on recommendation system methods

Intelligent Web personalization aims at adapting a user's interaction with the Web information space based on information gathered about the user. A complete automated Web personalization system is generally based on Web usage mining to discover useful knowledge about user access patterns, followed by a recommendation system to act on this knowledge in order to respond to the users' individual interest, in a manner transparent to the user, and while protecting the user's privacy and anonymity. The flow of information in a Web personalization system can be prone to significant amounts of error and uncertainty. This uncertainty pervades all stages from the user's Web navigation patterns to the final recommendations, including the intermediate stages of logging Web usage, preprocessing and segmenting Web log data into Web user sessions, clustering these sessions, and computing Web user profiles from these clusters. Fuzzy approximate reasoning can offer a general framework for the, recommendation process. It is this framework that is investigated in this paper. This paper presents a simple, intuitive, and fast approach to provide dynamic predictions in the Web navigation space. Real Web usage data is used as a simulation testbed for the fuzzy approximate reasoning based recommendation system.

An intelligent Web recommendation engine based on fuzzy approximate reasoning

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An intelligent Web recommendation engine based on fuzzy approximate reasoning