Collaborative Proposal Analysis

One of the features in the system is the rating option which subscribers can rate by their own favourites, so it would cause a conflict between the recommend system and rating program if the rating response declined.

For experimental purpose dataset has been obtained from MovieLens web site (http://movielens.org) which is collected and made available by GroupLens research. The MovieLens dataset includes 100,000 ratings ranging from 1 to 5, with a total of 943 users and 1682 films. In this research, randomly select 1100 users with more than 30 ratings. Also, LIBRARY THING dataset includes a book rating data set comprising more than 1.1 million ratings (scales 1-10) by 7279 users on 37,232 books. We have selected 5000 tags from the top to recommend books. The user-item dataset is available in the form of two dimensional

An overall review score is already assigned by the user when he/she writes the reviews. From these, the proposed probabilistic model calculates the aspect score for each aspect from the user's list of preferences and presents those reviews that has the best aspect score from different reviews. The model implicitly predicts the user aspects and their scores from the review provided by the user and then mines for the reviews that match the aspect score similar to those of the

PageRank is an algorithm developed by Sergey Brin and Lawrence Page which uses subset of network analysis to understand associations between nodes in a linked database. The algorithm uses multitude of parameters to assign PageRank to various pages and is the backbone to Google search functionality. Rank assigned to a webpage is calculated based on the ranks of the webpage citing it.

I also added the percentages for all experiences ranked over 4. The percentage of time I ranked myself as productive (ratings 5, 6, and 7) was 11.65% of the time. The percentage of time I ranked vitality as a 5, 6, and 7 was 12.06%. Finally, the percentage of time I ranked my experiences as pleasurable (ratings 5, 6, and 7) was 12.32%. I had more pleasurable experiences than productive or renewing experiences.

Recommender Systems are based on well-structured and incremental algorithms that have different strengths and weaknesses \cite{ricci2011introduction}. In general, the main existing RSs are concerned with improving accuracy with the premise of being useful to users. In this context, there is an increasing number of custom techniques that analyze the profile of the target user in order to better satisfy them \cite{bobadilla2013recommender}. In this sense, we conducted a broad study of the several metrics used in the literature to evaluate RSs. By organizational purposes, we divided these metrics into three groups: {\it Effectiveness-based}, {\it Complementary Dimensions of Quality} and {\it Domain Profiling}.\looseness=-1

From this we can see that if a review in the testing dataset contains the word fun, it would be classified as having a 5-star rating.

According to Nicole Landry, “online faculty rating sites are growing in popularity: 90% of the students in our sample reports visiting RateMyProfessors.com at least once” (Landry, Kurkul, Poirier 141). Professor rating websites are becoming a big factor in the college world. Students no longer have to worry if they chose a good professor or not. They may simply go to the rating websites and find the professor they are looking for and receive an idea of what the class will be like when the semester starts.

This essay aims to make an appraisal of two models of memory whilst weighing up the strengths and limitations of each. The two models of memory I will be evaluating are the Multy-store Model of Memory (MSM) and Level of Processing Model.

However, there are problems with the model of Atkinson and Shiffrin. The model is too simple and inflexible and fails to take account of factors such as the strategies people employ to remember things. It also places emphasis on the amount of information that can be processed rather than its nature. Some things are simply easier to remember than others, perhaps because they are more interesting, more distinctive, funnier, or whatever. The multistore model cannot account for this.

As can be seen in the table, a medium strength positive correlation which is highly significant was found between total scores in Agreeableness and Romance genre. The same medium positive correlation was found between Openness to Experience and Fantasy genre.

1. The memory of facts vary depending on the behavioral segmentation. If the loyalty status is high, the segmentation is apt to remember more facts about the product. This could be due to attending several in-home parties and hearing the information frequently. If the usage rate is heavy, the semantic memory could be high because this segment learns the facts firsthand – by using the products. If the user status is a non-user, then the semantic memory is pretty low.

The dataset contains numerous sets of content recommendations served to a specific user in a specific context. Each context (i.e. a set of recommendations) is given a display_id. In each such set, the user has clicked on at least one recommendation. The identities of the clicked recommendations in the test set are not revealed. Your task is to rank the recommendations in each group by decreasing predicted likelihood of being clicked.

In this paper, we present a survey of applications implementing collaborative filtering methods, content-based filtering methods and deep neural networks with Restricted Boltzmann Machines (RBMs) for generating recommendations. The drawbacks of collaborative and content-based methods is analyzed and proof of concept is provided for the need of deep learning based recommender systems. We introduce RBM algorithm and its applications for use in generic recommendation generation and propose to implement a deep learning neural networks algorithms(RBM) to create

A weighted hybrid recommender is one in which the score of a recommended item is computed from the results of all of the available recommendation techniques present in the system [40]. The score of different recommendation components is combined numerically. Weighted combination model which implements collaborative and content-based methods separately and combines their predictions with giving adjustable weight to both recommendation [50, 115].The benefit of a weighted hybrid is that all of the system’s capabilities are brought to bear on the recommendation process in a straightforward way and it is easy to perform post-hoc credit assignment and adjust the hybrid accordingly [40]. Liu et al. [116] proposed another hybrid recommendation system to deal with the problems of content-based, demographic filtering and collaborative filterings, such as sparsity and scalability. To resolve the sparsity problem, blanks in the user–item rating matrix are first filled with a weighted average rating on items which a user has rated, where the weight of the rated item is calculated by the similarity between an unrated item and the rated item in their feature values. Then, collaborative filtering