جستجوی مقالات مرتبط با کلیدواژه « Recommender System » در نشریات گروه « فنی و مهندسی »

Recently, recommender systems have expanded more and more as a new and fundamental technology to support users in choosing the right resources. These systems provide a personalized environment for selecting the desired resources by examining the past interactions of users and identifying interests. Of course, user behavior modeling and the recommendation mechanism are fundamental and decisive issues in the efficiency of recommender systems. In the field of e-commerce, the use of recommender systems plays an essential role in improving the user experience, attracting potential customers, increasing sales, and optimizing the efficiency of related service systems. Therefore, considering the importance of these systems in today's electronic businesses, knowing the functional dimensions of recommender systems is of particular importance. In this article, we are going to review the basic dimensions of recommender systems in the field of e-commerce and introduce some practical tools in this field. Certainly, by moving towards e-commerce systems based on intelligent recommender systems, we will witness huge and revolutionary changes in the infrastructure of the digital economy and related services.

The Internet of Things is an emerging information architecture based on the Internet that develops interaction between things and services in a safe and reliable environment. In fact, the purpose of this structure is to reduce the distance between the things of the physical world and information systems. In the Internet of Things, it is expected that intelligent devices will become active members in business and informational and social processes, so that they are able to interact between themselves and the external environment through the exchange of data and sensed information. In fact, the Internet of Things is a network of devices in which various things can communicate with other equipment with the help of computers and through Internet connections.  Recommendation technologies can help to more easily identify relevant artifacts and thus will become one of the key technologies in future IoT solutions.  The main task of recommender systems is to recommend service providers that meet the different needs of users. The paper porposes a Support Vector Machine (SVM) based algorithm and the TOPSIS multi-criteria decision-making model in order to create an effective recommender system and provide suggestions to users based on their preferences and increase user satisfaction. The experimental results show that the proposed recommender system can produce a series of objective recommendations that are effective based on accuracy and variety, novelty and high coverage. Finally, the results confirm the improvement in making recommendations .

Recommender systems are systems that, over time, learn what product(s) or item(s) each person or customer is (are) likely to like and recommend it (them) to him/her. These systems often operate based on similar behaviors from other (possibly similar) people. Finding similar people is generally a highly time-consuming process due to the large number of users and inaccurate due to the lack of information. For this reason, some methods have resorted to increasing speed. On the other hand, some other methods have added additional information so that they can increase the accuracy of finding similar or neighboring users. Some others have resorted to hybrid methods. Recently, by the use of basic clustering methods, which is based on finding the most similar neighbors with the help of users’ clustering, as well as by using basic content analysis methods and sometimes adding ontology to these methods, researchers have been able to take the advantage of these methods in order to solve some of the above challenges acceptably. In the proposed hybrid recommender system, we have used a two-stage system in which, in the first stage, two models of predictions are made, then in the second stage, by a combining component, the results of the first two parts are combined and the obtained results are given to us as the final results of the system. In the first part, a system based on imputation of missing values fills in the blanks in the scoring matrix. For this end, among the methods of the missing data imputation, we designed a method that was compatible with filling the data set in very sparse conditions, and then generalized it to our own method. In this regard, we have proposed a method based on the grey distance clustering. In the second part, which itself is a hybrid ontology-based recommender system, we first extract the information of each item with the help of a web crawler, then based on a basic article, we produce our own limited ontology, and after that we apply our proposed method. Then, with the help of a proposed method, we improve the ontology structure, thus increasing the accuracy of measuring semantic similarity between the items and users in later stages, and significantly improving the effectiveness of the created recommendations. It should be noted that this ontology is not comprehensive. Finally, we measure the similarity of item-items, user-users, and user-items using an innovative basic ontology similarity measurement method. By the use of this similarity matrix, we cluster users and items, and then store similar users and items as a new feature in the user/item profile for each user/item. This will help us speed up the process of looking for similar users and similar items in the future. In fact, based on this feature, we have increased the speed of the whole work. Since we have set our goal to build a system that makes a balance between the two criteria of accuracy and speed, we use these two criteria to evaluate the proposed system using a real data set. The results of comparing our proposed method with some up-to-date similar methods presented in this field (using the same data set) implies that our method is slower than fast methods, although it is more accurate than them. These results also suggest that the proposed method is faster than accurate methods and its quality is more competitive or even better than them.

Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can solve the problem of the cold start of the system and improve the quality of the search. In this research, a new method is presented in order to improve recommender systems in the field of the web, which uses the DBSCAN clustering algorithm to cluster data, and this algorithm obtained an efficiency score of 99%. Then, using the Page rank algorithm, the user's favorite pages are weighted. Then, using the SVM method, we categorize the data and give the user a combined recommender system to generate predictions, and finally, this recommender system will provide the user with a list of pages that may be of interest to the user. The evaluation of the results of the research indicated that the use of this proposed method can achieve a score of 95% in the recall section and a score of 99% in the accuracy section, which proves that this recommender system can reach more than 90%. It detects the user's intended pages correctly and solves the weaknesses of other previous systems to a large extent.

Today, tourism is one of the most lucrative industries in the world. Due to the large amount of information that exists about the points of Interest (POI) of a city, the tourist is faced with an overload of information. As a result, a recommending system is needed to recommend suitable tourist places to the tourist in the shortest time. In order to offer a better offer, the interests and context of the tourist should also be considered.In this research, a context-aware recommendation system, based on WEBGIS, has been designed and implemented for the city of Hamedan.In this system, according to the tourist's interests and his context condition, suitable POI are ranked and recommended to the tourist. Tourist interests are examined in ten categories, including recreational attraction, natural attraction, historical and cultural attraction and etc. In addition, in this study, the context of location, time and climatic conditions are considered. To recommend POI, a case-based technique is used that implements a specific style of content-based recommendations. One of the main advantages of this recommending system is the simultaneous attention to the tourist's interests and his tissue condition and offering the shortest path between the recommended places to the tourist. In addition to the suggested tourist attractions, other places including hotels and guesthouses, parking lots and cinemas are also displayed to the tourist in a clustered manner.

K-nearest neighbors (KNN) based recommender systems (KRS) are among the most successful recent available recommender systems. These methods involve in predicting the rating of an item based on the mean of ratings given to similar items, with the similarity defined by considering the mean rating given to each item as its feature. This paper presents a KRS developed by combining the following approaches: (a) Using the mean and variance of item ratings as item features to find similar items in an item-wise KRS (IKRS); (b) Using the mean and variance of user ratings as user features to find similar users with a user-wise KRS (UKRS); (c) Using the weighted mean to integrate the ratings of neighboring users/items; (d) Using ensemble learning. Three proposed methods EVMBR, EWVMBR and EWVMBR-G are presented in this paper. All three methods are user-based, in which VM distance is used as a measure of the difference between users / items, to find neighboring users / items, and then the weighted average is weighted, respectively. Also, weights based on the Gaussian combined covariance model are used to predict unknown user ratings. Our empirical evaluations show that the proposed method EVMBR, EWVMBR and EWVMBR-G, which utilizes ensemble learning, are the most accurate among the methods evaluated. Depending on the dataset, the proposed method EWVMBR-G managed to achieve 20 to 30 percent lower mean absolute error than the original MBR. In terms of runtime, the proposed methods are comparable to the MBR and much faster than the slope-one method and the cosine- or Pearson-based KNN recommenders.

In this research, a new method is presented to improve the recommendation systems in the field of health tourism, which can make accurate predictions by using participatory filtering and by using the points that previous tourists have given to places and health professionals in our country. For the use of tourists. According to the research, data clustering using DBSCAN algorithm obtained 99% efficiency score, which is the highest efficiency score among the existing algorithms. Also, SVM method has 95% score in accuracy section and 99% score in call section. Which shows the high accuracy of predicting the results and the proposed method in general up to 80% can correctly identify the places needed by the tourist and suggest the appropriate place to a large extent correctly

Recommender systems that predict user ratings for a set of items are known as subset of information filtration systems. They help users find their favorite items from thousands of available items. One of the most important and challenging problems that recommendation systems suffer from is the problem of dispersion. This means that due to the scatter of data in the system, they are not able to find popular items with the desired reliability and accuracy. This is especially true when there are a large number of items and users in the system and the filled ratings are low. Another challenging problem that these systems suffer from is their scalability. One of the major problems with these systems is the cold start. This problem occurs due to the small number of items rated by the user, i.e. the scatter of users. This problem is divided into two categories: new user and new item. The main focus of this article is on the problem of the new user type. This problem occurs when a new user has just logged in and has not rated any item yet, or when the user has already logged in but has been less active in rating. The goal is to address these three challenges. In this study, an ontology-based hybrid recommender system is introduced in which ontology is used in the content-based filtering section, while the ontology structure is improved by the collaborative filtering section. In this paper, a new hybrid approach based on combining demographic similarity and cosine similarity between users is presented in order to solve the cold start problem of the new user type. Also, a new approach based on combining ontological similarity and cosine similarity between items is proposed to solve the cold start problem of the new item type. The main idea of the proposed method is to extend users’/items’ profiles based on different mechanisms to create higher-performance profiles for users/items. The proposed method is evaluated in a real data set, and experiments show that the proposed method performs better than the advanced recommender system methods, especially in the case of cold start.

The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommender systems. Recently, many researchers have proved that using content models along with these systems can improve the efficacy of hybrid recommender systems. In this paper, we propose to use a new hybrid recommender system where we use a WordNet to improve its performance. This WordNet is also automatically generated and improved during its generation. Our ontology creates a knowledge base of concepts and their relations. This WordNet is used in the content collaborator section in our hybrid recommender system. We improve our ontological structure via a content filtering technique. Our method also benefits from a clustering task in its collaborative section. Indeed, we use a passive clustering task to improve the time complexity of our hybrid recommender system. Although this is a hybrid method, it consists of two separate sections. These two sections work together during learning. Our hybrid recommender system incorporates a basic memory-based approach and a basic model-based approach in such a way that it is as accurate as a memory-based approach and as scalable as a model-based approach. Our hybrid recommender system is assessed by a well-known data set. The empirical results indicate that our hybrid recommender system is superior to the state of the art methods. Also, our hybrid recommender system is more accurate and scalable compared to the recommender systems, which are simply memory-based (KNN) or basic model-based. The empirical results also confirm that our hybrid recommender system is superior to the state of the art methods in terms of the consumed time. While this method is more accurate than model-based methods, it is also faster than memory-based methods. However, this method is not much weaker in terms of accuracy than memory-based methods, and not much weaker in terms of speed than model-based methods.