Robust Preference Learning for Recommender Systems under Purchase Behavior Shifts

Although many approaches to collaborative filtering have been proposed, few have considered the data quality of the recommender systems. Measurement is imprecise and the rating data given by users is true preference distorted. This paper describes how item response theory, specifically the rating scale model, may be applied to correct the ratings. The theoretically true preferences were then used to substitute for the actual ratings to produce recommendation. This approach was applied to the Jester dataset and traditional k-Nearest Neighbors (k-NN) collaborative filtering algorithm. Experiments demonstrated that rating scale model can enhance the recommendation quality of k-NN algorithm. Analysis also showed that our approach can predict true preferences which k-NN cannot do. The results have important implications for improving the recommendation quality of other collaborative filtering algorithms by finding out the true user preference first.

The use of social robots in the domestic environment has increased during the past few decades. These robots are intended to maintain long-term interactions with humans while involving in a variety of tasks including daily activities, entertainment, and assisting elderly or disabled people. The ability to learn user's preferences and adapting interaction accordingly is a must for such robots. As the domestic environment consists of non-experts, social robots must possess more natural and human-friendly interaction capabilities. This paper presents an Autobiographical Memory(AM) based intelligent system which can learn user preferences through natural interactions and provide user adaptive services for each user in the multi-user domestic environment. The system is capable of learning user's preferences from hislher own statements and from another person's statements. Furthermore, the system is capable of adapting to user's hidden preference and changes of preferences easily. The robot's memory has been structured in such a way that it can easily remember the user groups and the relationship between users. This facilitates the robot for learning preferences which are common to a group of users. The system has been tested and validated using snack and beverage suggestion scenario.

Recently, recommender systems have been widely applied in E-commerce websites to help their customers find the items what they want. A recommender system should be able to provide users with useful information regarding their interests. The ability to immediately respond to the changes in user's preference is a valuable asset of recommender systems. This paper proposes a novel recommender system which aims to effectively adapt and respond to the immediate changes in user's preference. The proposed system combines IEC (Interactive Evolutionary Computation) with a content-based filtering method and also employs data grouping in order to improve time efficiency. Experiments show that the proposed system makes acceptable recommendations while ensuring quality and speed. From a comparative experimental study with an existing recommender system which uses the content-based filtering, it is revealed that the proposed system produces more reliable recommendations and adaptively responds to the changes in any given condition. It denotes that the proposed approach can be an alternative to resolve limitations (e.g., over-specialization and sparse problems) of the existing methods.

Due to the dynamic and heterogeneous nature of the Web, it is increasingly difficult for users to choose amongst large amounts of data. For this reason, the modelling of users and access to personalised information becomes essential. Recommendation systems stand out as systems that aim to provide the most appropriate and efficient services by offering personalised recommendations to users. Traditional recommendation systems provide suggestions to their users with static approaches and do not include user preferences that change over time in suggestion strategies. In this study, a comprehensive review and comparison of recommendation systems that can adaptively develop suggestion approaches according to changing user preferences and learn user preferences are presented.KeywordsCollaborative filteringClassification techniqueRecommendation systemSocial mediaUser preferences

Recommendation systems provide users with personalized recommendations, stand out as systems aiming to provide appropriate and efficient services. Traditional recommendation systems provide suggestions to their users with static approaches and do not include user preferences that change over time in suggestion strategies. In this study, a comprehensive review and comparison of recommendation systems that can adaptively develop suggestion approaches according to changing user preferences and learn user preferences is presented. In this research other approaches and solutions with frameworks of Deep Reinforcement Learning will be compared. In all articles that is reviewed Markov Decision Process (MDP) were used as a solution for dynamic recommendations and long-term rewards for the users. Proposed frameworks like DEERS, DRR etc. will be reviewed.

Critiques are employed as user feedback in critiquing-based recommender systems and they play an important role in the learning of user preferences, where recommender systems can gradually refine their understanding of user needs and provide better recommendations to users in subsequent interaction sessions. To reduce the effort of user interaction, the advantage of improving the recommendation efficiency by exploring relevant critiquing sessions in the interaction histories of other users has been recognized in recent studies of experience-based critiquing. In this study, we propose a novel approach for processing the historical interaction data in compound critiquing systems. In particular, we describe a history-aware collaborative compound critiquing method, which combines the strategies of preference-based compound critiquing generation and graph-based relevant session identification. Based on a simulation study using real-life data sets, we demonstrated that the proposed method outperformed other experience-based critiquing methods in terms of the recommendation efficiency. We also conducted a retrospective user evaluation, which confirmed the following observations: (1) incorporating user experience into compound critiquing systems significantly improves the performance compared with traditional unit critiquing systems; and (2) our graph-based session identification approach is superior to other baseline methods in terms of reducing the interaction effort of users.

Large language model (LLM) agents have emerged as a promising solution for enhancing recommendation systems via user simulation. However, existing studies predominantly resort to prompt-based simulation using frozen LLMs, which frequently results in suboptimal item modeling and user preference learning, thereby ultimately constraining recommendation performance. To address these challenges, we introduce VRAgent-R1, a novel agent-based paradigm that incorporates human-like intelligence in user simulation. Specifically, VRAgent-R1 comprises two distinct agents: the Item Perception (IP) Agent and the User Simulation (US) Agent, designed for interactive user-item modeling. Firstly, the IP Agent emulates human-like progressive thinking based on MLLMs, effectively capturing hidden recommendation semantics in videos. With a more comprehensive multimodal content understanding provided by the IP Agent, the video recommendation system is equipped to provide higher-quality candidate items. Subsequently, the US Agent refines the recommended video sets based on in-depth chain-of-thought (CoT) reasoning and achieves better alignment with real user preferences through reinforcement learning. Experimental results on a large-scale video recommendation benchmark MicroLens-100k have demonstrated the effectiveness of our proposed VRAgent-R1 method, e.g., the IP Agent achieves a 6.0% improvement in NDCG@10, while the US Agent shows approximately 45.0% higher accuracy in user decision simulation compared to state-of-the-art baselines.

In the field of artificial intelligence research, personalized recommendation technology has been rapidly developed. It can not only provide effective recommendation according to the preferences of system users, but also sort out the historical behavior and attribute information of users, and accurately calculate the content of user preferences. From the perspective of the development of the new media industry, in order to better meet the resource needs of system users, a resource recommendation system based on personalized recommendation technology can fully meet their needs for media reading. Therefore, on the basis of understanding the current research status of media resource recommendation system and technology, and according to the basic concept of graph neural network and the main structure of recommendation system, this paper mainly studies the application effect of the recommendation model of outburst neural network that integrates knowledge graph and time weight, so as to provide effective basis for media resource recommendation in the new era.

Sequential recommendation models aim to predict the interested items to a user based on his historical behaviors. To train sequential recommenders, implicit feedback data is widely adopted since it is easier to obtain than explicit feedback data. In the setting of implicit feedback, a user’s historical behaviors can be characterized as a chronologically ordered sequence of interacted items. From a perspective of machine learning, the historical interaction sequence and the recommended items can be considered as context and label , respectively, which are usually in one-hot representations in the recommendation models. However, due to the discrete nature, one-hot representations are hard to sufficiently reflect the underlying user preference, and might also contain noise from implicit feedback that will mislead the model training. To solve these issues, we propose a general optimization framework, Multi-View Smoothness (MVS), to enhance the smoothness of sequential recommendation models in both data representations and model learning. Specifically, with the help of a complementary model, we smooth and enrich the one-hot representations of contexts and labels to better depict the underlying user preference (i.e., context smoothness and label smoothness), and devise a model regularization strategy to enforce the neighborhood smoothness of the model itself (i.e., model smoothness). Based on these strategies, we design three regularizers to constrain and improve the training of sequential recommendation models. Extensive experiments on five datasets show that our approach is able to improve the performance of various base models consistently and outperform other regularization training methods.

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.

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.

This paper introduces Generalized Regression Neural Network (GRNN) for long term wind speed prediction of major wind power potential states in India. The performance of proposed GRNN model is evaluated using the publicly available online dataset of National Aeronautics and Space Administration (NASA). Data samples of 26 cities are used for training the generalized regression neural network and remaining 5 cities data samples are used for testing purpose. Air temperature, earth temperature, relative humidity, daily solar radiation, elevation, latitude, heating degree days, cooling degree days, frost days, longitude and atmospheric pressure are used as input variables. Mean square error between measured and forecasted wind speed using training data samples and testing data samples are found to be 0.000042279 and 0.1543. Here it is important to impart that the proposed GRNN model is trained and tested with data samples of different geographical locations in order to make it feasible for wind speed prediction of any other location. Wind power of prominent wind power potential states in India are predicted by a variable pitch and speed control wind turbine G80-2MW.

In a personalized recommender system of a ubiquitous computing environment, the decision on recommendation depends on some uncertain factors. Fuzzy system has an ability of solving the reasoning uncertainty, and gets widely used in the context awareness based personalized recommender system. In this paper, we present a fuzzy reasoning model based on the Fuzzy Petri Net. The model considers the requirements of task fulfillment, user's preferences and QoS of the system. With difference from those existing recommender systems, we focus on the formalized modeling of fuzzy reasoning of the personalized recommendation. Through formalization of fuzzy reasoning, we propose an approach to construct a rule-based reasoning system. The feasibility of the proposed approach is validated by an example of the personalized recommender engine.

This paper proposes user and item modeling methods towards recommender systems based on personal values. Marketing fields have been taking notice of personal values, because that such values are significantly related to user preference. While existing recommender systems usually employ user preference of items to make recommendations, proposed method focuses on users' personal values, which mean value judgments that show what attributes users put a high priority. The influence of each attribute on item evaluation is determined based on correspondence between ratings for item and the attribute. The proposed method is applied to actual review data, of which results supports the assumption that different users put high priorities on different attributes.

This paper proposes a recommender system based on personal-value-based user model. Conventional methods such as collaborative and content-based approaches tend to be less accurate for new users and items due to the lack of a relation between items and user preferences. While existing recommender systems usually employ user preferences of items for making recommendations, proposed method focuses on users’ personal values, which mean value judgments regarding on which attributes users put a high priority. The proposed recommender system employing personal-value-based user model is thus expected to realize more precise recommendations in cold-start situations. As one of typical cold-start situations, a prototype system is developed for recommendation using external resources. Experimental results show that generated user models reflect each user’s value judgment on attributes. In addition, the results also show that recommendation employing the proposed user model realizes improvements of precision in cold-start situations.