Personalized Recommendation System Research Articles

Rating Prediction for Mobile Applications via Collective Matrix Factorization Considering App Categories

The rapid development of Internet and mobile communication, smart phone, tablet computer and other mobile devices become widespread, they have become an indispensable part of life, changing the human lifestyle, way of working and learning style, it brings great convenience and fun to us. This paper mainly discusses the status of personalized recommendation system, mobile application and analyse the main problems of recommendation system. This research proposes a rising mobile application domain, which uses web crawling technique to get explicit feedback from a real-world mobile application market. The explicit feedback is the users’ ratings data on Apps and the corresponding category information. To establish a mobile application recommendation model based on user interest and categorization information and propose a novel collective matrix factorization algorithm. Through the crawled data, the improved algorithm, and the traditional collaborative. The traditional mobile application recommendation systems are mostly based on keyword search, popularity, download usage and categories, thus they do not make personalized recommendation to users, and invalid to find applications that users are interested in. Through the user’s historical behaviour, a personalized recommendation system can be established to recommend the applications that fit user’s interests effectively. This paper is based on the collective matrix factorization using user interest such as user’s ratings and category information, designs a personalized recommendation system for mobile applications by using the relationship between them.

User Behavior Identification and Personalized Recommendation Based on Web Data Mining

A good understanding of user behavior and consumption preferences can provide support for website operators to improve their service quality. However, the existing personalized recommendation systems generally have problems such as low Web data mining efficiency, low degree of automated recommendation, and low durability. Targeting at these unsolved issues, this paper mainly carries out the following works: Firstly, the authors established a user behavior identification and personalized recommendation model based on Web data mining, it gave the user behavior analysis process based on Web data mining, improved the traditional k-means algorithm, and gave the detailed execution steps of the improved algorithm; moreover, it also elaborated on the K nearest neighbor model based on user scoring information, the score matrix decomposition method, and the personalized recommendation method for network users. At last, experimental results verified the effectiveness of the constructed model.

IOT-Based Personalized products recommendation system

Recommendation technology is an essential component of the Internet of Things (IoT) services that can help users get information at any time and from any place. Traditional recommendation algorithms, on the other hand, are unable to satisfy the IoT environment’s swift and reliable recommendation criteria. The use of mathematical and information discovery methods to overcome the relationship with target consumers in order to have desired items is known as a recommendation system. In this paper, a recommendation algorithm based on collaborative filtering is proposed. In this sense, the recommendation method (Recommender Systems) was developed; it is focused on the user’s characteristics, such as hobbies, and it is recommended to satisfy the object’s user specifications, also known as customized recommendation system (Personalized Recommender Systems), The majority of modern e-commerce recommender programs tend to recommend the best goods to a customer, believing that each product’s properties remain constant. Some properties, such as price discounts, can, however, be customized to respond to the preferences of each customer..

Research on Personalized Recommendation System Based on Association Rules

With the rapid development of network technology, the amount of information is rapidly expanding, and the amount of various data has become huge and scattered. Using traditional keywords to retrieve search data has become quite time-consuming and difficult to focus on. Traditional search engines have been unable to help people effectively solve this problem, so personalized recommendation systems have emerged. In view of the user’s behavior and preferences, this article makes a detailed comparison of different recommendation technologies. It focuses on analyzing the applicability of the current popular content-based, association rules, and collaborative filtering recommendation technologies to the current needs. The technical scheme and system structure of recommendation system based on association rules are proposed, and aiming at the shortcomings of traditional recommendation system algorithms (the algorithm’s sparsity problem) and the current cold start problem of the recommendation system. Based on the MovieLens data set, a series of evaluation indicators are set up, comparative experiments are carried out, and the effectiveness of the proposed improved algorithm is analyzed.

Restaurant Recommendation System using Machine Learning

Nowadays a big challenge when going out to a new restaurant or cafe, people usually use websites or applications to look up nearby places and then choose one based on an average rating. But most of the time the average rating isn't enough to predict the quality or hygiene of the restaurant. Different people have different perspectives and priorities when evaluating a restaurant. Many online businesses now have implemented personalized recommendation systems which basically try to identify user preferences and then provide relevant products to enhance the users experience . In turn, users will be able to enjoy exploring what they might like with convenience and ease because of the recommendation results. Finding an ideal restaurant can be a struggle because the mainstream recommender apps have not yet adopted the personalized recommender approach. So we took up this challenge and we aim to build the prototype of a personalized recommender system that incorporates metadata which is basically the information provided by interactions of customers and restaurants online(reviews), which gives a pretty good idea of customers satisfaction and taste as well as features of the restaurant. This type of approach enhances user experience of finding a restaurant that suits their taste better. This paper has used a package called lightfm(the library of python for implementing popular recommendation algorithms) and the dataset from yelp. There are different methods of filtering the data, here we have used Hybrid filtering which is a combination of Content-based filtering (CBF) and Collaborative Filtering (CF). Since the results from Hybrid filtering are far more closer to accuracy than CBF or CF respectively. Then hybrid filtering gives results in the form of personalized recommendations for users after training and testing of the data

The graph-based behavior-aware recommendation for interactive news

Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user’s behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging user’s preferences. Further, although heterogeneous graphs have been applied in different areas, a proper way to construct a heterogeneous graph for interactive news data with an appropriate learning mechanism on it is still desired. To address the above concerns, we propose a graph-based behavior-aware network, which simultaneously considers six different types of behaviors as well as user’s demand on the news diversity. We have three mainsteps. First, we build an interaction behavior graph for multi-level and multi-category data. Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM to learn behavior sequence representations. Third, we introduce core and coritivity features for the behavior graph, which measure the concentration degree of user’s interests. These features affect the trade-off between accuracy and diversity of our personalized recommendation system. Taking these features into account, our system finally achieves recommending news to different users at their different levels of concentration degrees.

A genetic-based pairwise trip planner recommender system

The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball

Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability. To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. Furthermore, a hyperbolic attention network is employed to determine the relative importances of neighboring entities of a certain item. In addition, we propose an adaptive and fine-grained regularization mechanism to adaptively regularize items and their neighboring representations. Via a comparison using three real-world datasets with state-of-the-art methods, we show that the proposed model outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K recommendation.

Modeling High-order Interactions across Multi-interests for Micro-video Reommendation (Student Abstract)

Personalized recommendation system has become pervasive in various video platform.Many effective methods have been proposed, but most of them didn’t capture the user’s multilevel interest trait and dependencies between their viewed micro-videos well. To solve these problems, we propose a Self-over-Co Attention module to enhance user’s interest representation. In particular, we first use co-attention to model correlation patterns across different levels and then use self attention to modelcorrelation patterns within a specific level. Experimental results on filtered public datasets verify that our presented module is useful.

A Reciprocal Embedding Framework For Modelling Mutual Preferences

Understanding the mutual preferences between potential dating partners is core to the success of modern web-scale personalized recommendation systems that power online dating platforms. In contrast to classical user-item recommendation systems which model the unidirectional preferences of users to items, understanding the bidirectional preferences between people in a reciprocal recommendation system is more complex and challenging given the dynamic nature of interactions. In this paper, we describe a reciprocal recommendation system we built for one of the leading online dating applications in Japan. We also discuss the lessons learnt from designing, developing and deploying the reciprocal recommendation system in production.

Personalized recommendation system based on knowledge embedding and historical behavior

Collaborative filtering (CF) usually suffers from limited performance in recommendation systems due to the sparsity of user–item interactions and cold start problems. To address these issues, auxiliary information from knowledge graphs, such as social networks and item properties, is typically used to boost performance. The current recommended algorithms based on knowledge graphs fail to utilize rich semantic associations. In this paper, we regard knowledge graphs as heterogeneous networks to add auxiliary information, propose a recommendation system with unified embeddings of behavior and knowledge features, and mine user preferences from their historical behavior and knowledge graphs to provide more accurate and diverse recommendations to the users. Our proposed ReBKC shows a significant improvement on three datasets compared to state-of-the-art methods. These results verify the effectiveness of learning short-term and long-term user preferences from their historical behavior and by integrating knowledge graphs to deeply identify user preferences.

Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System

Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.

온라인 전시 행사에서 개인 맞춤형 추천 시스템 적용 사례 연구: 산학협력 EXPO를 중심으로

코로나 19의 확산으로 인해 많은 전시 · 행사가 온라인 비대면 형태로 진행되고 있다. 온라인 전시 · 행사는 오프라인 행사와 비교하여 공간과 시간 제약 없이 언제 어디서든 관람할 수 있는 장점이 있다. 반면 온라인 전시 · 행사는 관람객이 몰입하는 시간이 상대적으로 적기 때문에 콘텐츠 전달 효과가 떨어진다. 이러한 단점에도 불구하고 코로나 19의 확산세가 장기화하며 온라인 전시 · 행사는 확대되고 있다. 게다가 MICE 전문가들은 코로나 19가 종식된 이후에도 오프라인 행사와 온라인 행사를 병행하게 될 것으로 전망한다. 이에 본 연구에서는 온라인 전시 · 행사에서 프로그램에 대한 몰입감을 높일 수 있는 전략으로 개인 맞춤형 인공지능 추천 알고리즘을 적용하여 관람객이 머무는 제한된 시간 동안 콘텐츠 전달 효과를 극대화할 방안을 제안하고자 한다.

A neural network based price sensitive recommender model to predict customer choices based on price effect

The impact of price and price changes should not be ignored while designing algorithms for predicting customer choice. Consumer preferences should be modeled with consideration of price effects. Businesses need to consider for efficient prediction of an individual's purchase behaviour. Personalized recommendation systems have been studied with machine learning algorithms. However, the price-aware personalized recommendation has received little attention. In this paper, we attempt to capture insightful economic results considered in the marketing and economics disciplines by employing modern machine learning architecture for predicting customer choice in a large-scale supermarket context. We extract personalized price sensitivities and examine their importance in consumer behaviour. The employed data collected from a supermarket chain in Germany consists of implicit feedback based on customer-product interactions and the price of every interaction. We propose a two-pathway matrix factorization (2way-MF) model that is price-aware and tries to memorize customer-product interaction's implicit feedback. The proposed models achieve better model performance than standard Matrix Factorization models widely used in the industry. The approach was re-validated with data from supermarket chain in Taiwan. Other industries can adopt the proposed framework of modeling customer's preferences based on price sensitivity. We suggest that further research and analyses could help understand the cross-price elasticities.

Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients.

The COVID-19 pandemic has prompted an international effort to develop and repurpose medications and procedures to effectively combat the disease. Several groups have focused on the potential treatment utility of angiotensin-converting–enzyme inhibitors (ACEIs) and angiotensin-receptor blockers (ARBs) for hypertensive COVID-19 patients, with inconclusive evidence thus far. We couple electronic medical record (EMR) and registry data of 3,643 patients from Spain, Italy, Germany, Ecuador, and the US with a machine learning framework to personalize the prescription of ACEIs and ARBs to hypertensive COVID-19 patients. Our approach leverages clinical and demographic information to identify hospitalized individuals whose probability of mortality or morbidity can decrease by prescribing this class of drugs. In particular, the algorithm proposes increasing ACEI/ARBs prescriptions for patients with cardiovascular disease and decreasing prescriptions for those with low oxygen saturation at admission. We show that personalized recommendations can improve patient outcomes by 1.0% compared to the standard of care when applied to external populations. We develop an interactive interface for our algorithm, providing physicians with an actionable tool to easily assess treatment alternatives and inform clinical decisions. This work offers the first personalized recommendation system to accurately evaluate the efficacy and risks of prescribing ACEIs and ARBs to hypertensive COVID-19 patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10729-021-09545-5.

Dynamic Diet Planner: A Personal Diet Recommender System Based on Daily Activity and Physical Condition

Background and ObjectivesAt the beginning of human civilization, human being needs to undergo immense physical labor to survive. During those days high-calorie diet was essential. However, the evolution of technology has changed the scenario and much lesser amount of physical labor is required to survive in modern hi-tech days, requiring less amount of calorie. The excess calorie gets deposited in the body in the form of white fat. As a consequence, obesity appears as an epidemic all over the world. Long term obesity causes many diseases like heart attack, stroke, diabetes, and many other ailments. It is tough for people, who honestly want to check their obesity by controlling food habits, to continue a static diet chart with fixed restricted foods for a long time. Most of the time, people give up such restrictions on food. It will be very useful if a system exists, that will generate a dynamic diet chart based on calorie spend by the body. MethodsThis research describes a system that will generate a dynamic diet chart based on calorie spend by the body and other data like user's BMI, food preferences, etc. In this system, user can tailor his/her diet chart by changing daily physical activities (activities that burn calories). The main contributions of the proposed system are as follows:•A dedicated hardware system to quantify physical labor during walking and running throughout the day from feet pressure changes along with walk cycle detection.•The proposed hardware system can distinguish between walk and run.•A dynamic diet chart preparation system, where the user has the provision to plan his/her diet chart with food affinity. The entire work is divided into two modules; the first one focuses on the hardware design to detect and quantify physical activities during “walking” and “running”. These data are used in the second part. The second part contains calorie calculation along with the preparation of a dynamic diet chart with food affinity. In the hardware design, an Application Specific Integrated Circuit (ASIC) is proposed to measure the physical activity during “walking” and “running”. ResultAt the time of hardware logic synthesis, VHDL and FPGA are used. Experimental results show that the proposed hardware provides more accurate results than a pedometer. There is no standard metric to measure the performance of the dynamic diet chart generation system. Competency of the dietitian at the time of setting the parameters, honesty of the users at the time of the interactive session, etc. are the main factors that influence heavily the performance of the dynamic diet chart preparation system. The advantage of dynamic diet chart is that users can modify the diet menu as per their wish, and the system generates a diet chart in such a way that calorie intake (through food items) is maintained properly. In addition to this, the user can plan his future meals. DiscussionThis work is an interdisciplinary work. It is a blending of electronics (FSM), design, and computation for decision making; the computation portion includes software engineering, soft computing, etc. Future plan of this work is to integrate this system with “Cloud” to provide different data analysis related services, like region-wise food habit pattern prediction, health-related statistics, etc.

Hybrid collaborative recommendation of co-embedded item attributes and graph features

In recent decades, personalized recommendation systems have attracted much attention from multiple disciplines for recommending interested products and services to users. Recommendation accentuates both the importance of feature learning tasks and the challenges posed by the sparsity of rating matrix. A common method for addressing the sparsity problem is to extend the feature space by the attributes of users and/or items. However, there are two main drawbacks in most existing recommendation methods. The first is the high computational cost of most existing recommendation models when using additional information from users and/or items to expand the feature space. The second problem is that it is difficult to obtain user additional information due to the high cost of acquiring tag knowledge and the increase in user privacy awareness. In this paper, we propose a novel and simple model to address the abovementioned issues, which employs a semi-autoencoder to co-embed the attributes and the graph features of the items for rating prediction (short for Item-Agrec). More specifially, a semi-autoencoder is introduced to learn the hidden nonlinear features of items for achieving a low computational cost, and thus the proposed Item-Agrec model can flexibly use side information from different sources. Meanwhile, in the case that it is not easy to obtain the user’s additional information, we take the item’s graph features and attributes into consideration for improving the accuracy of recommendation. Experiments on several real-world datasets demonstrate the effectiveness of the proposed Item-Agrec compared with state-of-the-art attribute-aware and content-aware methods.

TDD-BPR: The topic diversity discovering on Bayesian personalized ranking for personalized recommender system

The main objective of the recommender system is to determine user behavior or preference and suggest the items of interest to the user. We notice that topic information is one of the significant factors that enables a user to choose the next items. To mimic this behavior, we design a topic diversity discovering (TDD) model to capture user preferences on topics. Unlike the previous studies applied the topic information to recommender systems, which only considered the item topics. In this paper, we not only focus on the item topic information but also consider the user topic information on the recommender system. We develop a TDD model to learn the topic information from the item, and we then utilize the transfer learning to obtain the user topic information. We then integrate the TDD model with the Bayesian personalized ranking (BPR) model to build a novel TDD-BPR recommender system. We experiment on three well-known three datasets MovieLens-1 M, Pinterest, and TMDB dataset, which achieved 71.8%, 91.2%, and 96.1% in the Hit Ratio@10. Empirical evidence showed that TDD-BPR discover the potential topic information to understand the user’s preferences and get better recommendation performance.

Developing a personalized recommendation system in a smart product service system based on unsupervised learning model

Contemporary consumers have begun shifting their focus from product functionality toward the value that can be derived from products. In response to this trend, companies have begun using product service systems (PSS), business models that provide customers not only with tangible products but also with intangible services. Moreover, with the increasing use of smart devices, services providers can offer customized services to customers based on user-generated data with smart product service systems (Smart PSS).Despite extensive research on Smart PSS framework, few of these frameworks treated customer as an active data producer, which means producing data for the Smart PSS actively. Additionally, most of them proposed a general solution instead of a personalized one. To bridge the research gap, this study proposed a method that includes: (1) unsupervised natural language processing (NLP) methods to analyze user-provided data. (2) a recommendation system integrating deep learning to offer customers with personalized solutions. Thus, the role of customers is not only a service receiver but also an active data producer and forms a value co-creation process with service providers. A case study of tourist recommendation validate the benefits of proposed method. The main contribution of this research is to develop a personalized smart PSS method which could achieve a win-win situation for all players in this method.

Toward a Knowledge-based Personalised Recommender System for Mobile App Development

Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users’ query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user’s query with the minimum mismatches.