Survey on Service Based Ratings of Users by Exploring Geographical Location

Recommendation systems help online users with advantageous access to the items and services they may be intrested on this present reality. Because of the requirements of compelling forecast and productive recommendation, it is advantageous for the location-based services (LBS), to discover the user's next location that the user may visit. So in this paper, diverse kinds of methodologies used to discover, anticipate, and examine location based services are talked about. It is important to convey those expectation and recommendation services for ongoing real time application with direction mapping. While considering location information's, at that point the information measure ended up noticeably colossal and dynamic. Finding ideal answer for anticipate the rating in view of the location and unequivocal conduct is overviewed

A Novel Contextual Information Recommendation Model and Its Application in e-Commerce Customer Satisfaction Management

In the current supply chain environment, distributed cognition theory tells us that various types of context information in which a recommendation is provided are important for e-commerce customer satisfaction management. However, traditional recommendation model does not consider the distributed and differentiated impact of different contexts on user needs, and it also lacks adaptive capacity of contextual recommendation service. Thus, a contextual information recommendation model based on distributed cognition theory is proposed. Firstly, the model analyzes the differential impact of various sensitive contexts and specific examples on user interest and designs a user interest extraction algorithm based on distributed cognition theory. Then, the sensitive contexts extracted from user are introduced into the process of collaborative filtering recommendation. The model calculates similarity among user interests. Finally, a novel collaborative filtering algorithm integrating with context and user similarity is designed. The experimental results in e-commerce and benchmark dataset show that this model has a good ability to extract user interest and has higher recommendation accuracy compared with other methods

A Collaborative Filtering Recommender System For Infrequently Purchased Product Using Slope-One Algorithm And Association Rule Mining

Nowadays, tourism industry are actively being utilised in generating a state or country income. In order to attract tourist from all over places, information conveyance is important. Traditionally, people travels to certain places based on oral recommendation by families and friends. Now, people tends to go travel based on reviews that are read from blogs and websites. But, this leads to overflow of unfiltered information. In order to effectively recommending places to travel for tourist, recommendation engine are being developed. Most recommendation engine has suffice information to make recommendation for example Amazon.com recommendation and Google.com recommendation. Meanwhile, in tourism it is quite challenging in making recommendation because hotels are occasionally being booked or purchased by consumer. This is due to the fact that travelling are expensive and time consuming. This project implement the collaborative filtering using slope-one algorithm and also implement association rule mining in recommending hotels for tourist. This recommender system uses slope-one algorithm whereby it accumulate and takes into account of the difference in popularity. The objective of this project to study different types of recommendation techniques for infrequently purchased products and to investigate technique and dataset that are suitable to implement in recommending infrequently purchased products. As a conclusion, this collaborative filtering recommendation system will help user in decision making. Further research on other approaches in implementing recommender system in tourism domain can help in information delivery

Evaluating recommender systems from the user's perspective: survey of the state of the art

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users' perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS's ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users' adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system's recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system's overall perceptive qualities and how these qualities influence users' behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing method

The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric

Whereas many food recommender systems optimize for users’ preferences, health is another but often overlooked objective. This paper aims to recommend relevant recipes that avoid nutrients that contribute to high levels of cholesterol, such as saturated fat and sugar. We introduce a novel metric called ‘The Cholesterol Factor’, based on nutritional guidelines from the Norwegian Directorate of Health, that can balance accuracy and health through linear re-weighting in post-filtering. We tested popular recommender approaches by evaluating a recipe dataset from AllRecipes.com, in which a CF-based SVD method outperformed content-based and hybrid methods. Although we found that increasing the healthiness of a recommended recipe set came at the cost of Precision and Recall metrics, only putting little weight (10-15%) on our Cholesterol Factor can significantly improve the healthiness of a recommendation set with minimal accuracy losses.publishedVersio

A Survey of Sequential Pattern Based E-Commerce Recommendation Systems

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems