Personality Based Recommendation System Using Social Media

Recommendation system is the reason of success for most of the social media companies as well as e-commerce sites. Giving recommendation to the uses is one of the interesting and challenging tasks nowadays, it helps to generate revenue, to increase number of users, to reduce the searching time for particular item. Recommendation system helps for making interest in user and eventually it increases the popularity of any site. Huge number of items (product, users, movies, songs, hotels etc.) and its feature sets makes it hard to predict the accurate items to the user. It is important to keep all historic data of user as well as all information about the items to generate recommendation. In this paper, the personality of the user is used with the combination on the most popular recommendation techniques like collaborative filtering (CF) and content based filtering (CB) proposed on the amazon review data set. In the first model the personality of the user is calculated by using the big five model on the twitter account. In the second module Collaborative filtering is used to generate the recommendation based on the historic information of the user wherries in third module, Content based filtering is uses to generate recommendation based on the feature set of the item. Pearson-correlation algorithm is applied on both modules and ranking are generated. Finally union of the both vector space are taken as the final recommendation

Preference Aware Service Recommendation Using Collaborative Filtering Approach

Service recommendations are shown as remarkable tools for providing recommendations to users in an appropriate way. In the last few years, the number of customers, online information and services has grown very rapidly, resulting in the big data analysis problem for service recommendation system. Consequently, there is scalability and inefficiency problems associated with the traditional service recommendation system which suffers in processing or analyzing large-scale data. Moreover, most of available service recommendation system gives the same rankings and ratings of services to different users without any considerations of many user’s preferences, and hence it fails to reach user’s personalized requirements. In this paper, we have proposed a Preference-Aware Service Recommendation method, to overcome the above challenges. It aims at recommending the most appropriate and preferred services to the users and provide a personalized service recommendation list in an effective way. Here, users' preferences are captured as keywords, and a user-based Collaborative filtering approach is adopted to create appropriate recommendations. A widely-adopted distributed computing platform, Hadoop is used for the implementation of this approach, which improves its efficiency and scalability in big data environment, using the MapReduce parallel processing method. DOI: 10.17762/ijritcc2321-8169.150510

Implementation of Collaborative Filtering Approach in Preference Aware Service Recommendation

Service recommendations are shown as remarkable tools for providing recommendations to users in an appropriate way. In the last few years, the number of customers, online information and services has grown very rapidly, resulting in the big data analysis problem for service recommendation system. Consequently, there is scalability and inefficiency problems associated with the traditional service recommendation system which suffers in processing or analyzing large-scale data. Moreover, most of available service recommendation system gives the same rankings and ratings of services to different users without any considerations of many user’s preferences, and hence it fails to reach user’s personalized requirements. In this paper, we have proposed a Preference-Aware Service Recommendation method, to overcome the above challenges. It aims at recommending the most appropriate and preferred services to the users and provide a personalized service recommendation list in an effective way. Here, users' preferences are captured as keywords, and a user-based Collaborative filtering approach is adopted to create appropriate recommendations. A widely-adopted distributed computing platform, Hadoop is used for the implementation of this approach, which improves its efficiency and scalability in big data environment, using the MapReduce parallel processing method. DOI: 10.17762/ijritcc2321-8169.15034

Recommendation system using autoencoders

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.This research has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D UnitsProject Scope: UIDB/00319/202

Intent-Aware Contextual Recommendation System

Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve. Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness (or scoring) is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations. We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining (ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field cannot be longer than 1,920 characters," the abstract appearing here is slightly shorter than the one in the PDF fil

Improvement of recommender systems considering big data of users’ comments on chosen items

Regarding to the increase in the online social networks services during the recent years, the recommender system has turned into an emerging research subject. Currently, regarding to the fast and consistent expansion of using the internet, the necessity of a recommender system for refining the large volume of data has increased greatly. The purpose of recommender systems is to provide a list of the interested items for the user and due to the increase in the current data volume, the previous used tools are nor suitable for processing this data volume; hence, having a system which can save and process the large data has turned into a problem. In this study, to solve the mentioned problems, a system is recommended using a model-based collaborative filtering refinement model which uses the Spark processing model in Hadoop context in order for more exact advising the user from the viewpoint of the users. The obtained results indicate that the current method will be more efficient and effective compared to the common recommendation methods.Keywords: recommender systems; big data; sentiment analysis; hadoop; spark