Domain Analysis Paper

With the help of recommender systems, users are suggested or provided with most significant recommendations that meet their tastes or needs. Our categorization distinguishes content-based and collaborative filtering approaches to establish relations between domains recommendation model. An introduction is given for the Cross Domain recommender system. A lot future work will to be done under the cross domain recommender system.

The program created category similarity analysis which compared the products with similar category, a sales rank comparison of two or more products which views the sales rank and checks for similar ranks of the purchase and sees if one is preferred over the other. The program also checked for title similarity, average rating, and reviews of similar

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

We will briefly touch on the important aspects of the domain of the requirement as understanding domain is one of the critical pieces from a system design perspective as it allows us to pursue an appropriate architecture. (Reference Source: Esposito and saltarello. Microsoft .NET - Architecting Applications for the Enterprise, 2nd Edition)

With the increasing number of objects, in the web, the necessity for a recommender is sensed to catch relevant and preferred objects in a large space. Whenever people desire to purchase the product or select something amongst many things, they have to make a decision what item to select. Their choice depends on others because they may ask people for the recommendation or know their opinion indirectly. We often get recommendations from trust people like our family for choices that we do not have any experience on (Resnick & Varian, 1997). “Word-of-mouth” opinion the method that most of the people use/d is the oldest version of recommendation, for instance, you may ask your friends who book they suggest you to read

Usually the data mining analysis is done by grouping commonly co-occuring things (Associations), discovering time-ordered events (Sequences), anticipating future occurences (Predictions), identifying natural groupings of items (Clusters) and finally, by uncovering generalizations to help classify items (Classification). These different type of mining usually take a lot of time and a good understanding of the business and

To fulfill all the requirements, Boots decided to use Customer Data Analysis System (CDAS) by giving advice from IBM. According to the support of this system, most queries response times were 30 times faster than before even though the database has reached 1.200 GB. Because of this, the analysts of Boots were delighted. CDAS includes IBM’s intelligent Miner for Data being used for more advanced data mining such as segmentation and

Abstract - In the Data mining process, we can identify the patterns in the data that is hard to find using normal analysis. Several Mathematical and statistical algorithms are used in this approach to determine the probability of the event or scenario. The main aim of this process in terms of technical representation is to find the correlation amongst the attributes. There is a huge amount of discovery being carried out in this field creating a huge scope and jobs in this area. Several data mining algorithms are present that could determine different features present in the data that could lead in prediction and future analysis. Main Study report would consist of these algorithms that could help us predict and some sample data that we

The authors[7] presented an approach in which ontological profiles are built. Ontology is considered to be a hierarchy of topics which is used to classify and categorise web pages. It is also used to identify the topics in which the particular user is interested. Ontology has some existing concepts to which interest scores are assigned. Keeping the reference ontology, these profiles are maintained and updated. With observing the ongoing behaviour, a spreading activation algorithm was proposed for maintaining the interest scores. So this way of the interest scores updation, the most relevant results are brought on the top.

• Characterization: is a summary of common features of items in a target class, and yields what is known as characteristic rules. The information pertinent to a user-specified class are usually retrieved by a database request and run through a summarization segment to mine the soul of the data at diverse levels of mining. For example, one may want to illustrate the OurVideoStore clienteles who frequently lease more than 30 movies a year. With conception chain of command on the traits describing the objective class, the trait based induction technique can be used, for example, to carry out data summarization.

When faced with large number of choices people often turn out for some kind of recommendations, for example people will refer to newspaper for the movie review, a travel guide for tour insights. Similarly we are interested in

In our future work, we will be calculating the values of support and confidence on the multimode cluster using Apriori pruning. In this algorithm every item is considered as a 1-itemset. The support for each item is calculated and compared against the input support value entered by the user. If the support values of the item sets are less than the input support then those candidates are discarded and the action rules discovery algorithm only runs on the remaining attributes. This process is continued for 2–item sets and 3 and so until we have the support values for the item sets less than the input value. For example if we get the input support value to be 5 and the 4 item set attributes lead to support value less than 5, then we will be considering only the attributes that form the 4 item sets. In this method, we are pruning the attributes before the action rules are discovered and the algorithm only run on the attributes that form the frequent item sets. This avoids unnecessary combination of rules within the attributes. Instead of generating the rules at a later stage after finding all the combinations of rules, this prunes early to avoid unnecessary combinations. This greatly increases the time complexity of the algorithm and as well as space complexity. We are also planning to run this algorithm in the MapReduce Framework similar to the one we have done in this paper. This further decreases the time complexity and the action rules discovery

Following the submission of our project proposal a month ago, our project has changed course. Given our team 's relative inexperience in the field of recommendation systems, we are now interested in conducting an analysis to fully understand how the various recommendation methods and techniques perform with mutliple datasets.

The usability testing for PowerTeacher’s Gradebook indicated that the interface is lacking in some basic usability features such as learnability, ease of use, feedback and memorability. The study findings suggest many usability issues related to creating new assignments, entering comments, manually overwrite grades, and generating reports within the school information system which are potentially impeding a teacher’s capacity to efficiently execute routine classroom management tasks, at least the time frame in which these tasks are completed. This is especially a problem for novice or intermediate teachers, and essentially teachers who do not ample planning time, as they are occupied with multiple functions and responsibilities. The

For instance, if a customer has placed a few products in her shopping basket, the recommender system may recommend complementary products to increase the order size. Item-to-item correlation recommender systems can be Automatic, if they are based on observations of the customer’s unchanged behavior. They can also require some Manual effort, if the customer must explicitly type in several items of interest in order to generate a recommendation. Item-to-item correlation recommender systems are usually ephemeral, since they do not need to know any history about the customer to generate a recommendation based on the products the customer has