PERSONAL RECOMMENDER SYSTEMS
MAN562
February 18, 2015

Abstract. In the recent years, the web searches have witnessed a dramatic change where the people have had so much on the internet such that the searches have become extremely difficult. The users of the internet on a regular basis have found it difficult to use and find the required information. There has been also the claim that some of the data sources are unpopular which makes the finding of the information very difficult. The personal recommender system is responsible for building the gap between the objects and the users. The system recommends to the users the areas they might be interested in during browsing. The system is responsible for solving the problem that is being experienced today of information overload (Hang, Hsiao, 2013). There are different algorithms, and they are responsible for producing the different systems. There is the system that collaborates and filters, the system that is based on content, the hybrid system, and the structure based system. All the four systems are determined by the different algorithms operating in each (Pu, Chen, & Hu, 2012).

Introduction This recommender system is a subclass of the information filtering system that is responsible for predicting and rating the weight of an item as per the user requirements. In the recent years, the systems have become extremely important and are applied in a variety of areas. These areas include books, movies, and news. As the user searches a given book, then the system is responsible for giving the various options to a book or the goods he is searching. The systems have simplified the work of searching on the internet. The recommender systems are also available for experts, jokes insurance, online dating and twitter followers (Pu, Chen, & Hu, 2012). There are major two ways that the recommender system shows its results. One of the ways is collaborative and also the content based filtering. The collaborative system bases its research and recommendations from the users past behavior or past searches and uses this to base its search results. It gives search results related to the previous searches done by the user. The content-based system gives the users areas that are related to his field of search and gives the related fields. The most recent system is the one that combines the two and is called the hybrid based system. There are, however, distinct differences that can be seen in the two types of systems (Hang, Hsiao, 2013). The collaborative system requires a huge base of past information in order to get its search results. The content-based system does not require a wide range of past information. This is among other distinctions that can be seen in the two systems. These systems have become a useful alternative to the search algorithms. The systems help the users discover new items that might not have been found by them by giving the consumer a broad variety of alternatives. The systems have developed due to the recent wave of the internet and the World Wide Web. There has been a huge use of the internet where people have been using the internet to search for products and find more about particular products. This has been the major cause that has led to the development of the recommender systems (Pu, Chen, Kumar, 2008). There may be visible examples such as Amazon.com that has been a major bookshop that has been doing all its business on the internet. Another example is the famous search engine google.com. All this have adopted the recommender system to help and simplify the work of its customers. The recommender systems have a wide range of objectives. They find out the products that the internet users are interested in. they turn the internet users to customers. They are a key to improving sales in the e-commerce websites. They have a responsibility of increasing the customer loyalty towards a particular product or brand of products. Their system must make recommendations that are satisfying to the users so that they do not lose their customers due to lack of satisfaction (Hang, Hsiao, 2013). The system will help the user determine on the best alternatives available for the hundreds of alternatives available. They provide the information to all the users in accordance with their personal tastes (Pu, Chen, & Hu, 2012).
Literature review. The ability of computers to provide solutions was discovered years back in the year of computing. Grundy, a librarian, was the first person to start the recommender systems. He grouped all the users into stereotypes and grouped the stereotypes. He did this using the hard coded information that he interviewed the users. In the early 1990 there was the beginning of the collaborative filtering that would deal with the high overload of the available online spaces (Pu, Chen, Kumar, 2008). There later followed the automatic collaborative system that was used to locate the relevant opinions automatically. A scholar known as GroupLens used this information to locate Usenet articles on the internet. This period is when the computer system became an area of interest to many computer users. This interest came up with different systems at that time. These interests were the Ringo and the Bell Core among others (Hang, Hsiao, 2013). During the 1990s, the deployment of technology to the systems began and was applied, and a good example is the Amazon.com. The research in this field of recommender systems intensified when the Netflix started the Netflix Prize in 2006. The competition was to build a more complex recommender system that was better than the existing ones by 10%. They were competing on creating accurate recommendations for their users (Marsk 2008). Over the last twenty years, there has been a wide range of research on how to recommend things to people over a variety of options that are available. There has been a handbook for recommender system that has been established that has a wide range of recommender systems to choose from. The problems that are surrounding recommendations have made it possible to develop different algorithms to deal with the problems. The system should interact with the user so that it learns the requirements of the user and recommends for the user in future (Pu, Chen, Kumar, 2008).

Methodology The methodology used is the search or the use of the users of the internet to come up with the list of challenges that have been encountered in the recommender systems. The methodology consists use of questionnaires on the internet users and the use of the comment boxes similar to the suggestion boxes in the real life situations (Hang, Hsiao, 2013). There has been a team of experts that has been chosen to go through all the comments that the users post and conclude from it whether the systems are good or bad. The research has also been based on the use of the internet questionnaires. The users are to feel the questionnaires on the internet which are then to be analyzed and later give the required inferences from what the customers say. This two are the major methods that have been used in this research (Pu, Chen, & Hu, 2012). However, it is significant to make a note of the lot of challenges to the above methodologies. Some of the customers comment falsely to the questionnaires, and it is important to be extremely careful with such information that might not get us to the desired goals and objectives. The above methodologies have been able to come up with the challenges that the system have been posting to the users (Pu, Chen, Kumar, 2008). Challenges of the recommender systems The systems have had a challenge to the users. The major challenge is the conflict that arises between the users and the systems. The system might not be satisfying the user requirement that becomes a major challenge. The system might be offering products that are different from the ones that the user requires (Ricci 2011). The wisdom of the crowds that the collaborative filtering has been taking advantage of has been made simpler by the use of data collecting opportunities that the web can afford. There are the users whose behavior can be modeled but other users will have behaviors that cannot be modeled. This is a major challenge facing the model and its implementation. This is because this kind of users will exhibit typical behaviors at all times. This makes it difficult to model his behavior. The user has the challenge of trying to make the system understand his requirements (Pu, Chen, & Hu, 2012). The other challenge is where the users can skew the results of the system and hence tamper with the efficiency of the system. The users can also exploit the recommender system and make it favor one product more than the other. This will be as a result of the positive feedback that the users give of a particular product and the negative feedback that they give of the alternative products. This makes it is difficult, and the system will always display the product that has the positive feedback. The system that is good and can correct such issues. However, this has been a major challenge to the systems over the past years (Pu, Chen, Kumar, 2008). The other challenge is the large scale of data and the information that is available. The algorithms that are designed for the small amounts of data have a difficult time when it comes to managing the huge amounts of data that are coming up. This becomes a challenge to these systems. The algorithms have a difficulty keeping up with the challenge of huge data. This will, however, require more specialized approaches for the scenarios that are in real time. There is also the confront of the isolation protection among the different systems that are available. The patterns that are identified by the recommender system algorithms might be difficult to the users and them strength not even be acquainted with such challenges exist. There has been a recent company that has had the ability to calculate the prediction score of a habit based on the products that a person purchase. A father who was in frequent use of the targeted ads was able to learn that the daughter was pregnant. The system was very accurate since it could predict the day when the mother would give birth based on the products that the mother was purchasing (Ricci 2011). The other challenge is exploration versus exploitation. The systems find it challenging when they are trying to deal with the new products and the new users in the market. This is because the system has no enough information about the product that he can base his argument and give options for the product. To deal with the challenges, the system will provide the users with the options of the different products he might be searching for (Pu, Chen, Kumar, 2008). The other challenge is the guided navigations provided by the recommendations. The navigations should guide the user to the product that the recommender system wants the user to buy. The navigation should be directed towards the direction that the user cares about. It should take into consideration the moods tags of the users (Hang, Hsiao, 2013). The other challenge is the time value. The users have different tastes and preferences. There are the users that require only a small insight to a reading while there are those that will require deeper reading. There are persons who are engrossed in meaningful the most recent information and others like job search are not recent information (Ricci 2011). Recent trends in technology used in collaborative systems. This system has a wide use in the e-commerce business. The collaborative filter system has no direct requirements to the project. This system can recommend the objects without a clear content description. There are many collaborative filter applications that have come up with the recent changes in technology. The recent trends have been used to predict the consumer behavior. The collaborative systems have had in the recent past a bank of all the customer needs and requirements. The system is responsible for placing the needs in the order of preference and provides each customer with the services they require in the order of preference (Pu, Chen, Kumar, 2008). The other advancement in technology is the introduction of more complex algorithms that are now able to handle more customer issues than the traditional algorithms that handle the small amounts of data only. This has provided the customer with a wide range of variety which they can now choose from and make more informed decisions on the products they will purchase (Ricci 2011). The other advancement in technology has been the use of simpler statistical models that have been incorporated into the algorithms. Some other recent advancement in technology includes models to analyze the redundancy analysis and the spurs data analysis. The CFS ha had a new way of providing new information to the users even when the interests have not been discovered by the users themselves. The CFS also has the objects that are in a position to show the objects that are difficult to express structurally. This recent changes in technology have enabled this system understand its users better.
The recommendation filtering technology intensified in the early 1990 when the Amazon.com technology came up. The systems have increased the sales volume of the customers since it recommends to them the products that they may purchase other than the usual products. Several companies have been offering their recommendation technology for the online users (Ricci 2011).
Conclusion
The recent rise in the usage of the internet to search for goods and commodities by the users has been a wake-up call for the users of the internet to provide recommendations on their products. The recommender technology has been a necessity for any business that will flourish. The systems should be made more accurate as possible to serve the customer requirements. The systems should come up with better algorithms that will support more data in the systems (Pu, Chen, Kumar, 2008).

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