Search Engine Optimization Of A Business On E Business

Bi et al \cite{Rec:Bi} provides ranked related entities to the user query along with the results of the main entity. In order to do this, this articles makes use of user's search history, click history and knowledge base. A matrix is created comprising of the user information which connects to the entities along with the ranking, click results. A tri-linear function\cite{Rec:Bi} is defined mapping these details and which will be used to rank the related entities

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

E - Business presents a great opportunity to and organization to increase sales and enter into a much larger

I am choosing to do my Business Analysis paper on e-commerce. I will explain the importance of it as well as the effects on the global economy. I will discuss the advantages of telecommunications and information technologies in a business versus those businesses without e-commerce. I will also discuss the marketing strategies involved with e-commerce and how it helps businesses. Due to the global nature of internet business, electronic commerce (e-commerce) standards have become a priority on the national and international level. While most traditional businesses are subject to local, state, and national

Personalized message and recommendation to customer based on previous purchase history and activity on the site

Content-based filtering methods are based on a description of the item and a profile of the user’s preference. In a content-based recommender system, keywords are used to describe the items; beside, a user profile is built to indicate the type of item this user likes. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past.

Reading the cases about E-Biz in Baltzan (2013), the examples of the “Million Dollar Homepage” and the “One Red Paperclip” (pp. 198-200), really sends the entrepreneurial mind spinning. The thoughts of “How else can one use the internet to raise money?”, “Which businesses might benefit from e-trade on the internet that are not already there?”, and “What other disruptive, nontraditional ways might the internet be used?”, all immediately flood the brain after reading the two examples presented in this exercise. Well, presented here is this author's response to the exercise.

statistical analysis) in contrast with personalized methods recommend item/s without considering any personal information or previous actions of users. In other words, it does not care about the taste of individual customers. For example, recommending a newly released movie or the most famous movie to all users. This type of recommendation is automatic and ephemeral (Schafer, Konstan, & Riedl, 1999). Personalized recommendation methods may require the user to be logged in, store user profile and have different comfortable suggestions for each user depending on their desires and how they behave in the past. This type of recommendation is persistent personalization. The collaborative filtering method is an example of personalized advice. Furthermore, a recommendation may be based on current session, does not need to store user profiles and have the same recommendation for all users. This type of recommendation is non-persistent and non-ephemeral. An example of this kind of recommendation is content-based filtering recommendations. (Kazienko & Ko\lodziejski, 2005; Schafer et al., 1999)

Have you ever purchased any product on the Internet, used the Internet to collect information or data, or played computer games on the Internet? You must agree that it is fast, easy, and enjoyable. The Internet has been a part of our daily life for several years now. In addition, in the business world, a new business model, E-business and E-commerce, has appeared for several years. According to Ali, there are two main types of E-commerce: B2B and B2C (2000). One is business to business (B2B). This means that enterprises use the Internet to transact or trade between business operations and their partners. Another is business to consumer (B2C). In other words, enterprises provide products, support good, and services to the customers on the

All of the known recommendation techniques have strengths and weaknesses, and many researchers have chosen to combine techniques in different ways. Hybridization can alleviate some of the problems associated with other recommendation techniques. This system combines different recommender systems to improve information retrieval and gain better performance with fewer of the drawbacks of any individual one and also it can avoid the major disadvantages of other methods. The combination can be done using several alternatives, for example: weighted, switching, mixed, feature combination or augmentation or cascading. Meta techniques avoid the problem of sparsity by compressing ratings over many examples into a model, which can be more easily compared across users. Weighted, Mixed, Switching and Feature Combination. With these hybrids, it does not make sense to talk about the order in which the techniques are applied .The main shortcoming of hybrid methods, however, is increased implementation complexity and expense [14]. Table 10 proposes an overview of hybrid Recommender systems and its

To predict which recommendation is most likely to be clicked, we started off by looking into the dataset for this challenge. After going through the dataset we discussed how we would go about reading the page views log, which is 100GB uncompressed. The data size is huge to work on one’s laptop. The model that we plan to build will be trained on a subset of the data locally, inclusive of all the features. Then we had planned to use IU supercomputers to train the model on all the data.

Over specialization: Content-based recommenders have no inherent method for finding something unexpected. The system suggests items whose scores are high when matched against the user pro le, hence the user is going to be recommended items only similar to those already rated. This drawback is also called serendipity problem to highlight the tendency of the content-based systems to produce recommendations with a limited degree of novelty [4].

Organizations conduct transaction on a global scale, and they must adapt to the advances in technology to compete and succeed. E-commerce will provide a platform to promote growth and

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

In (Narducci et al., 2015) they recommend hospitals and doctors that perfectly fits a particular patient profile by representing a semantic recommendation system. The system has come up with HealtNet (HN) which is a core component of social network in which this recommender system is embedded. HealthNet’s goal is similar to PLM in terms of finding similar patients, sharing knowledge as well as looking for the experience. In addition to these features, HN has implanted recommender system which finds out the similarity between patients, recommends hospitals and patients that best fit to their profile using the community data. The first similarity between different patients are calculated using algorithm and then generates list of hospitals and doctors according to their rank