Does Daily Travel Pattern Disclose People’s Preference?

Existing studies normally focus on extracting temporal or periodical patterns of people’s daily travel for location based services. However, people’s characteristics and preference are actually paid much more attention by business. Therefore, how to capture characteristics from their daily travel patterns, is an interesting question. In order to address the research question, we first develop two basic measures in terms of repetitiveness of travel and then two advanced measures, to capture people’s activity of daily travel, and the colorfulness of lifestyle, respectively. Incorporating historical trajectories, with real-time positions from a location-based social network (LBSN), i.e. Foursquare, we conduct statistical analysis for people’s travel patterns in US cities. Finally, we illustrate people’s profiles of travel patterns and lifestyles. Results show that people’s preference can be inferred from the developed activity and colorfulness measures. Those findings demonstrate that proposed measures are supposed to be effectively adopted for researchers on travel pattern analysis and preference analysis, and further give suggestions to individuals for location-based decision making

System Oriented Social Scrutinizer: Centered Upon Mutual Profile Erudition

Social recommender systems are getting up more attention for product advertisement and social connectivity. A good recommender               should think about the system and the user. The user will have a preference list of some items and these preferences can be useful in suggesting the things which can help the endorsing system to identify better items. In this paper, the idea of social recommender systems as a pattern matching and regular expression making is used for unification of similarities. The concept of mutual profile pattern expression can be applied on various networking platforms. In these type of shared platforms, people all around the globe share resources and interact with each other. In order to manage or scrutinize users according to their interests and likeness, the mutual profile pattern of users can be used. Further predicting of membership function is performed to show how much extent does the profile matches

Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities

Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabilities. The extra information (prediction reliabilities) can be used in a variety of relevant collaborative filtering areas such as detection of shilling attacks, recommendations explanation or navigational tools to show users and items dependences. Additionally, recommendation reliabilities can be gracefully provided to users: “probably you will like this film”, “almost certainly you will like this song”, etc. This paper provides the proposed neural architecture; it also tests that the quality of its recommendation results is as good as the state of art baselines. Remarkably, individual rating predictions are improved by using the proposed architecture compared to baselines. Experiments have been performed making use of four popular public datasets, showing generalizable quality results. Overall, the proposed architecture improves individual rating predictions quality, maintains recommendation results and opens the doors to a set of relevant collaborative filtering fields

HYBRID RECOMMENDER SYSTEM USING SINGULAR VALUE DECOMPOSITION AND SUPPORT VECTOR MACHINE IN BALI TOURISM

When going to make a visit to a tourist area, tourists must determine the place they want to visit. Meanwhile, the desired place has several categories and types. The many types of tourist attractions make tourists confused in determining their choice. Therefore, we focus on developing a hybrid recommendation system by combining several recommendations approaches, namely collaborative filtering, content-based filtering, and demographic filtering. This recommendation system was built to solve the cold start problem that often appears in collaborative filtering and content-based filtering. In this study, weighted and switching techniques were chosen as the hybridization method. These two techniques are used to overcome the weaknesses of each technique so that it becomes a better recommendation system. The singular value decomposition (SVD) algorithm was chosen to be used in collaborative filtering, meanwhile, content-based filtering uses the calculation of cosine similarity values , and demographic filtering uses the support vector machine (SVM) algorithm. The data used in this study is data on tourist destinations in the Bali area obtained from crawling on the TripAdvisor site. In this study, the root mean square error (RMSE) and mean absolute error (MAE) was used to measure the accuracy of the resulting rating prediction. The results of the experiments carried out show that the hybrid method that was built produces better accuracy prediction results than when run separately with an average mean absolute error (MAE) of 0.6660 and a root mean square error (RMSE) of 0.8644

Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System

The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative filtering. However, consideration of item features to find the nearest neighbor can be a more judicious approach to increase the proportion of similar users. In this study, we offer a new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed framework uses rated items of the similar feature of the ’most’ similar individuals, instead of using the wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens datasets and the experimental results corroborate our anticipations

A New Approach for Movie Recommender System using K-means Clustering and PCA

Recommendation systems are refining mechanism to envisagethe ratings for itemsand users, to recommend likes mainly from the big data. Our proposed recommendationsystem gives a mechanism to users to classify with the same interest. This recommendersystem becomes core to recommend the e-commerce and various websites applications basedon similar likes. This central idea of our work is to develop movie recommender system withthe help of clustering using K-means clustering technique and data pre-processing usingPrincipal Component Analysis (PCA). In this proposed work, new recommendationtechnique has been presented using K-means clustering, PCA and sampling with the help ofMovieLens dataset. Our proposed method and its subsequent results have been discussed andcollation with other existing methods using evaluation metrics like Dunn Index, averagesimilarity and computational time has been also explained and prove that our technique isbest among other techniques. The results achieve from the MovieLens dataset is able to provehigh efficiency and accuracy of our proposed work. Our proposed method is able to achievethe MAE of .67, which is better than other methods