Enhancing Recommendation System using Adapted Personalized PageRank Algorithm

A location-based orientation-aware recommender system using IoT smart devices and Social Networks

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.

Developing recommender systems with the consideration of product profitability for sellers

Consumers often learn the weights they ascribe to product attributes (“preference weights”) as they search. For example, after test driving cars, a consumer might find that he or she undervalued trunk space and overvalued sunroofs. Preference-weight learning makes optimal search complex because each time a product is searched, updated preference weights affect the expected utility of all products and the value of subsequent optimal search. Product recommendations, which take preference-weight learning into account, help consumers search. We motivate a model in which consumers learn (update) their preference weights. When consumers learn preference weights, it may not be optimal to recommend the product with the highest option value, as in most search models, or the product most likely to be chosen, as in traditional recommendation systems. Recommendations are improved if consumers are encouraged to search products with diverse attribute levels, products that are undervalued, or products for which recommendation-system priors differ from consumers’ priors. Synthetic data experiments demonstrate that proposed recommendation systems outperform benchmark recommendation systems, especially when consumers are novices and when recommendation systems have good priors. We demonstrate empirically that consumers learn preference weights during search, that recommendation systems can predict changes, and that a proposed recommendation system encourages learning.The data files and online appendix are available at https://doi.org/10.1287/mksc.2018.1144 .

Modeling user preferences using neural networks and tensor factorization model

In order to overcome the problems of poor operation function and low recommendation accuracy of the traditional high-quality curriculum resource recommendation system, this paper proposes and designs a new high-quality Curriculum Resource Recommendation System for educational technology specialty. With the support of the system framework, the system hardware module is designed, mainly including user registration and login module and high-quality course resource recommendation module, so as to realize the recommendation function of the system. The system software part constructs the user’s high-quality curriculum resources mining classification tree according to the user’s preference attribute value of high-quality curriculum resources, and obtains the recommended basic data. On this basis, the user’s preference for course resources is calculated, so as to complete the recommendation of high-quality course resources. The experimental results show that compared with the traditional recommendation system, the functions of the designed system have passed the test and have high recommendation accuracy.

Recommender system is an effective way to help users to obtain the personalized and useful information. However, due to complexity and dynamic, the traditional recommender system cannot work well in mobile environment. In this paper, we propose a restaurant recommender system in mobile environment. This recommender system adopts a user preference model by using the features of user's visited restaurants, and also utilizes the location information of user and restaurants to dynamically generate the recommendation results. Baidu map cloud service is used to implement the proposed recommender system. The result of a case study shows that the proposed restaurant recommender system can effectively utilize user's preference and the location information to recommend the personalized and suitable restaurants for different users.

Since user preference is drifting over time, modeling temporary dynamic recommender system has been proven to be valuable for accurate recommendation performance. However, user feedback is continuously updating while the traditional recommender system is trained off-line in batch mode so that it cant capture user taste change in time. In this paper, we build a dynamic real-time recommendation model based on implicit user feedback stream to improve both the recommendation accuracy and training efficiency. Moreover, our model has obvious advantages over the traditional approaches in diversity, interpretability, and strong robustness to hostile attack. Finally, we conduct experiments on two real world datasets to validate the effectiveness of our proposed method and demonstrate the superior performance when compared with state-of-the-art approach.

Considerable attention has been given to processing graph data in recent years. An efficient method for computing the node proximity is one of the most challenging problems for many applications such as recommendation systems and social networks. Regarding large-scale, mutable datasets and user queries, top-k query processing has gained significant interest. This paper presents a novel method to find top-k answers in a node proximity search based on the well-known measure, Personalized PageRank (PPR). First, we introduce a distribution state transition graph (DSTG) to depict iterative steps for solving the PPR equation. Second, we propose a weight distribution model of a DSTG to capture the states of intermediate PPR scores and their distribution. Using a DSTG, we can selectively follow and compare multiple random paths with different lengths to find the most promising nodes. Moreover, we prove that the results of our method are equivalent to the PPR results. Comparative performance studies using two real datasets clearly show that our method is practical and accurate.

Today, advertisement has become a part of media content and it is exposed to viewers in ways such as home shopping, PPL (Product Placement), and T-commerce. Even though media contents and platforms have been developed into user-interactive services, the interaction between advertisement systems and users is still limited. For advertising services to interact with users, a recommendation system should be introduced. Recommendation systems have been studied such as content-based, collaborative filtering, and hybrid methods. Since conventional recommendation systems require high computational complexity in the preference prediction process and do not consider the time factor, there are limitations in performing recommendation considering user preference. In this paper, we propose an advertisement recommendation system that predicts the ATU’s (Active Target User) preference for items in real-time. The proposed system creates PRETREE by modeling ATU's history data with a tree data structure for reducing computational complexity. A recommendation is triggered when the item that the ATU is looking at in the media content is detected. If there are rating records for the item in the history of the ATU, the preferences are predicted on the basis of PRETREE. If there is no history for the item, the proposed system predicts preference on the basis of similar groups and the LCC (Longest Common Category) node at the ATU's PRETREE. Finally, we evaluate the performance of the proposed recommendation system using real- world data.

A multi-theoretical kernel-based approach to social network-based recommendation

The rapid evolution of the Internet has resulted in the availability of huge volumes of online learning resources on the web. However, many learners encounter difficulties in retrieval of suitable online learning resources due to information overload. Besides, different learners have different learning needs arising from their differences in learner’s context and sequential access pattern behavior. Traditional recommender systems such as content based and collaborative filtering (CF) use content features and ratings, respectively, to generate recommendations for learners. However, for accurate and personalized recommendation of learning resources, learner’s context and sequential access patterns should be incorporated into the recommender system. Traditional recommendation techniques do not incorporate the learner’s context and sequential access patterns in computing learner similarities and providing recommendations; hence, they are likely to generate inaccurate recommendations. Furthermore, traditional recommender systems provide unreliable recommendations in cases of high rating sparsity. In this paper, we propose a hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners. In our recommendation approach, context awareness is used to incorporate contextual information about the learner such as knowledge level and learning goals; SPM algorithm is used to mine the web logs and discover the learner’s sequential access patterns; and CF computes predictions and generates recommendations for the target learner based on contextualized data and learner’s sequential access patterns. Evaluation of our proposed hybrid recommendation approach indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.

Learning instrumental variable representation for debiasing in recommender systems.

In the realm of recommender systems, the prediction of diverse customer preferences has emerged as a compelling research challenge, particularly for multi-state business organizations operating across various geographical regions. Collaborative filtering, a widely utilized recommendation technique, has demonstrated its efficacy in sectors such as e-commerce, tourism, hotel management, and entertainment-based customer services. In the item-based collaborative filtering approach, users’ evaluations of purchased items are considered uniformly, without assigning weight to the participatory data sources and users’ ratings. This approach results in the ‘relevance problem’ when assessing the generated recommendations. In such scenarios, filtering collaborative patterns based on regional and local characteristics, while emphasizing the significance of branches and user ratings, could enhance the accuracy of recommendations. This paper introduces a turnover-based weighting model utilizing a big data processing framework to mine multi-level collaborative filtering patterns. The proposed weighting model assigns weights to participatory data sources based on the turnover cost of the branches, where turnover refers to the revenue generated through total business transactions conducted by the branch. Furthermore, the proposed big data framework eliminates the forced integration of branch data into a centralized repository and avoids the complexities associated with data movement. To validate the proposed work, experimental studies were conducted using a benchmarking dataset, namely the ‘Movie Lens Dataset’. The proposed approach uncovers multi-level collaborative pattern bases, including global, sub-global, and local levels, with improved predicted ratings compared with results generated by traditional recommender systems. The findings of the proposed approach would be highly beneficial to the strategic management of an interstate business organization, enabling them to leverage regional implications from user preferences.

Each study program requires students to take several elective courses. The appropriateness of the elective courses taken with the student's abilities can be one of the factors for the success of student studies. This research focuses on building a content-based filtering recommendation system that provides several elective courses recommendation according to the student's academic history. The proposed recommendation systems' results are based on preprocessed word items from courses taken by the user. The weighted cosine similarity between the elective courses syllabus and the user profiles is calculated. Moreover, the experiment employed a dataset of the CSUGM course syllabus. The proposed recommendation system is evaluated in two ways, i.e., questionnaire method and validation method. The questionnaire method obtains an assessment of system performance, hence the validation method to get the average accuracy. The questionnaire was conducted by involving thirty students of the CSUGM undergraduate program. The experimental results show that the proposed recommendation system has a good performance proven by the percentage of recommendation diversity 81.67%. Furthermore, the accuracy of the proposed recommendation system has an average of 64%.