Nowadays, products are offered to buyers in different varieties and qualities on the Internet environment. Recommedation systems are needed to make the right choices and make effective decisions. The article offers a new method and algorithm based on data obtaned through different ways and by creating hybrid recommendation systems. Estimation method is given based on information about objects and users for proper development of the algorithm. The proposed method can identify the proximity between the users group and the objects the users are interested in.
In modern days, majority of transaction operations are realized by using digital commersion methods over the internet. Person who wants to obtain product looks over suitable internet shop sites and advertising product sites [1, 2]. Taking a glance at advertising product sites, it is possible to encounter names of different products and services which have analogical functions and identification, but the different prices and characteristic. Lack of information about interesting product, outnumber variety of offerred products on the internet environment lead to wrong decision-making.
In the modern times, social networks have spesific roles for recommedation. Improving the digital trade, advertising sites, searching systems make easy for obtaining information about product and services, simultaneously, dynamic change of data makes users select the product [3].
Another interesting fact is a consumer, who offers products and services, sometimes acts as a buyer. Taking into account the current situation, the dynamic changes and right decision-making become difficult for companies in the market. They try to improve by learning their mistakes [4]. On the Internet, recommendation systems are widely used for helping customers and users.
Recommendation systems are software tools for recommendation, finding, identifying service, products, objects which may be interesting for users [2]. Then, information is identified taking into account its relevance and importance according to the user’s interests. Multidisciplinary knowledge of people is used during creation of recommendation systems. Artificial intelligent, interactions of human and computers, information technologies, data mining, statistics, adaptive user interfaces, making decision systems, marketing and other fields are included to these areas [2]. Recommendation systems are created based on two strategies.
Here profiles of users and objects which may be interested them are created. Users can enter demographic data to their profiles. Profiles of objects reflect different attributes depending on their types. Recently, recommendation systems are applied to areas which are not specific for them. For example, the application of recommendation systems in solution of diagnostic issues of complex technical systems [5].
Hybrid recommendation systems increase the effectives of recommendation systems by combining advantages of collaborative algorithms and content – based algorithms. While the complexity of the system increases, the punctuality of the potential recommendations may increase. In the case of information lack, the advantage of the hybrid method may assert itself for collective filtering. When applying the hybrid method, firstly the content is measured on the basis of content filtering and then from these dimensions, mixture of collective filtering is created. As a result, a data set can be formed on the interests of a specific user’s activity [6-9].
Recommendation systems can use different algorithms. Obtained results may change depending on the specific issue and the relations of data set. Regardless of the use of any type of method or algorithm, the recommendation systems use the following considerations in the recommended elements:
In practice, it can be observed that a user evaluates the objects differently rather than any other object. Therefore, this evaluation should be considered more informative. If any given evaluation of object is too low or too high than the average evaluation for these objects, these products will be suspicious. A sharp difference in prices should be explained seriously. In that case, recommendation systems should ensure decision making based on the interests of the users.
The purpose of this article is solving the following issues:
When designing the recommendation system, first of all, it is necessary to pay attention to the evaluation of the results. There are different criteria for evaluating the results. Innovation, accuracy, surprising possibility, robustness to external forces, persistence, and etc. are included here. The accuracy criterion is widely used in practice and it shows how close the given predictions are to the results which may be accepted as an etalon.
After determining the evaluating criteria of results, users’ evaluation for different objects and things are studied. The line and column vectors of the future value matrix are generated from the values of other users for services and things. At this time, it should be taken into consideration that offering same products or services by various companies, manufacturers are referred to as different objects. On the other hand, the recommendation system is not created in general, but in a specific area. These systems are created for enterprises selection in the field of education; attractive tourism facilities, routes in tourism; selection of services and prices in healthcare.
Different directional and characteristic recommendation systems can be created in accordance with each field. In user profiles, different sides of any object may be evaluated in different aspects. It is possible to create a set of values by a user’s values for various objects depending on objects selection in different sources. Users can respond to different questions during visiting sites. Given values about the usefulness of objects and importance can be used without additional operations over the given values. These types of values can be saved by calculating as average value related on specific time sequences. An average value and the number of the elements generating this value can be saved as a two-dimensional massive in accordance with the time interval. In this case, the two-dimensional time sequence can be written as:
If the recommendation system encounters any value that related to any previous time interval in practice, then it can recalculate the number and average value at that time interval. If the newly found value is included in the ith time interval and value is , then and can be calculated again.
The texts that express the attitude of the user to objects can be divided into different parts at each time interval. It may be important to divide a user’s comments due to objects and form of value for each object. The methods which are applied for forming user’s evaluation in the articles can be used in the entry of recommendation systems, avoiding errors during user enter name for receiving advice on information about objects, things, services. The system can choose the word or phrase combination that is most closely related to its keyword, comparing it with its keywords in its library. If necessary, the user can be required to confirm the found word. As a result, it can be searched according to found word and can generate recommendation. For this purpose, two dimensions, two sets, or two vector proximity can be used.
In simple cases, Jaccar proximity can be used. This measurement is known proximity measures in science; however it is widely used in informatics, molecular biology, ecology, bioinformatics and other areas.
Let’s review the proximity of two sentences or two articles instead of two words. Our goal is to generate possible numerical values from the user’s object description of the text. There are methods and algorithms, which are used with numerous words and phrases for solving analogical issues in literature [10]. The comments posted by the user on the webpage, messages sent by SMS, or textual responses to the inquiries are small in size.
Therefore, these texts can be stored and processed with different algorithms. First of all, the alphabet should be created to form the evaluation. For example, the weight vector (p1, p2…..pk) for positive attitudes can create relation alphabet by using these words – “excellent”, “good”, “useful”, “advantageous”, “sufficient” or for negative attitude, it is possible to create relation alphabet with internal weight vector (m1,m2… mk) by using “worst”, “bad”, “useless”, “disadvantageous”, “harmful”. Another alphabet used in practice can also be applied. The effectiveness of this alphabet created during experimental experiments can be determined and corrected. In order to correct the alphabet, some ideas of users about objects can be analyzed by experienced experts.
Thus, the value given by the user to the object from the text source (Txt array) can be formed by the following algorithms:
It is known that the recommendation systems form the current value, taking into account the customer’s previous values, and this current value is included in the user value array. The issue of forming a current value from the time sequences value may be calculated as follows:
The values given to the object at different times and in different sources by the user is taken into consideration. This formula prefers the latest value given by the user. Analyzing the passport data of objects can also provide valuable information. Included information is the warranty period and duration for objects, things, and services. First of all, frequency of occurrence of the name of the object as the results of trade operations, advertising sites, comments by users, print products and in other digital and non-digital advertising methods may be included here.
If the name of the analyzed object in the different methods is greater than others, then this frequency may indicate the importance of the object for users. But sometimes one of the two objects frequencies can be much higher than the frequency of the other. Then, it may be important to reduce the difference between them.
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