Annually, WEB search engine providers spend a lot of money on re-ranking DOCUMENTs in search engine result pages (SERP). Click models provide advantageous information for re-ranking DOCUMENTs in SERPs through MODELING interactions among users and search engines. Here, three modules are employed to predict users' clicks on SERPs simultaneously, the first module tries to predict users' click behaviors using Probabilistic Graphical Models, the second module is a Time-series Deep Neural Click Model which predicts users' clicks on DOCUMENTs and finally, the third module is a similarity-based measure which creates a graph of DOCUMENT-query relations and uses SimRank Algorithm to predict the similarity. After running these three simultaneous processes, three click probability values are fed to an MLP classifier as inputs. The MLP classifier learns to decide on top of the three preceding modules, then it predicts a probability value which shows how probable a DOCUMENT is to be clicked by a user. The proposed system is evaluated on the Yandex dataset as a standard click log dataset. The results demonstrate the superiority of our model over the well-known click models in terms of perplexity.

The main purpose of this project was to design a WEB-based health DOCUMENT automation program for fitness clubs. This research was carried out using information such as fitness features of the clubs and the types of users. The existing literature on this subject was reviewed to reflect the limitations and suggestions of previous studies in the program design. The types of services and required processes were identified by field surveys, and program designing continued in both the database and user sections. Creating a WEBbased context, predicting user education and the possibility of developing a smart phone app were among the factors that were considered by previous reviews of similar healthrelated software. The results of this design can help improve specialized services. This design can also be employed at the University Health Center to determine the essential characteristics of each level of user.

The principal aim of a search engine is to provide the sorted results according to the user’ s requirements. To achieve this aim, it employs the ranking methods to rank the WEB DOCUMENTs based on their significance and relevance to the user’ s query. The novelty of this work is to provide a user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the ranking system is considered as the agent of the learning system and the selection of DOCUMENTs is displayed to the user as the agent's action. The reinforcement signal in this system is calculated based on the user's click on the DOCUMENTs. Action-values in the RRLUFF algorithm are calculated for each feature of the DOCUMENT-query pair. In the RRLUFF method, each feature is scored based on the number of the DOCUMENTs related to the query and their position in the ranked list of that feature. For learning, the DOCUMENTs are sorted according to the modified scores for the next query. Then according to the position of a DOCUMENT in the ranking list, some DOCUMENTs are selected based on the random distribution of their scores to display to the user. The OHSUMED and DOTIR benchmark datasets are used to evaluate the proposed method. The evaluation results indicate that the proposed method is more effective than the related methods in terms of P@n, NDCG@n, MAP, and NWN.