1,599 research outputs found
Applying Recommendation Techniques In Conventional Grocery Retailing
In grocery retailing, promotions and recommendations, derived from traditional data mining techniques, apply uniformly to all customers and not to individual ones, thus failing to meet each customer’s personal needs. On the other hand, recommender systems have been widely explored in the field of e-commerce managing to provide targeted personalized recommendations for products and services. Despite the great success of recommender systems in internet retailing, their application in many other fields remains practically unexplored. RFID and pervasive networking technologies now offer the potentials to utilize recommender systems in physical environment. The scope of this paper is to examine the individual characteristics of the new domain along with the applicability of various recommendations techniques. The results indicate the superiority of the e-commerce recommendation techniques against the traditional approaches currently used in grocery retailing
Conformative Filtering for Implicit Feedback Data
Implicit feedback is the simplest form of user feedback that can be used for
item recommendation. It is easy to collect and is domain independent. However,
there is a lack of negative examples. Previous work tackles this problem by
assuming that users are not interested or not as much interested in the
unconsumed items. Those assumptions are often severely violated since
non-consumption can be due to factors like unawareness or lack of resources.
Therefore, non-consumption by a user does not always mean disinterest or
irrelevance. In this paper, we propose a novel method called Conformative
Filtering (CoF) to address the issue. The motivating observation is that if
there is a large group of users who share the same taste and none of them have
consumed an item before, then it is likely that the item is not of interest to
the group. We perform multidimensional clustering on implicit feedback data
using hierarchical latent tree analysis (HLTA) to identify user `tastes' groups
and make recommendations for a user based on her memberships in the groups and
on the past behavior of the groups. Experiments on two real-world datasets from
different domains show that CoF has superior performance compared to several
common baselines
Overcoming data sparsity
Unilever is currently designing and testing recommendation algorithms that would make recommendations about products to online customers given the customer ID and the current content of their basket. Unilever collected a large amount of purchasing data that demonstrates that most of the items (around 80%) are purchased infrequently and account for 20% of the data while frequently purchased items account for 80% of the data. Therefore, the data is sparse, skewed and demonstrates a long tail. Attempts to incorporate the data from the long tail, so far have proved difficult and current Unilever recommendation systems do not incorporate the information about infrequently purchased items. At the same time, these items are more indicative of customers' preferences and Unilever would like to make recommendations from/about these items, i.e. give a rank ordering of available products in real time.
Study Group suggested to use the approach of bipartite networks to construct a similarity matrix that would allow the recommendation scores for different products to be computed. Given a current basket and a customer ID, this approach gives recommendation scores for each available item and recommends the item with the highest score that is not already in the basket. The similarity matrix can be computed offline, while recommendation score calculations can be performed live. This report contains the summary of Study Group findings together with the insights into properties of the similarity matrix and other related issues, such as recommendation for the data collection
Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk
Statistics based privacy-aware recommender systems make suggestions more
powerful by extracting knowledge from the log of social contacts interactions,
but unfortunately, they are static. Moreover, advice from local experts
effective in finding specific business categories in a particular area. We
propose a dynamic recommender algorithm based on a lazy random walk that
recommends top-rank shopping places to potentially interested visitors. We
consider local authority and topical authority. The algorithm tested on
FourSquare shopping data sets of 5 cities in Indonesia with k-steps of 5,7,9 of
(lazy) random walks and compared the results with other state-of-the-art
ranking techniques. The results show that it can reach high score precisions
(0.5, 0.37, and 0.26 respectively on precision at 1, precision at 3, and
precision at 5 for k=5). The algorithm also shows scalability concerning
execution time. The advantage of dynamicity is the database used to power the
recommender system; no need to be very frequently updated to produce a good
recommendation.Comment: 6 page
Challenges in context-aware mobile language learning: the MASELTOV approach
Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment
Implementation of a personalized food recommendation system based on collaborative filtering and knapsack method
Food recommendation system is one of the most interesting recommendation problems since it provides data for decision-making to users on selection of foods that meets individual preference of each user. Personalized recommender system has been used to recommend foods or menus to respond to requirements and restrictions of each user in a better way. This research study aimed to develop a personalized healthy food recommendation system based on collaborative filtering and knapsack method. Assessment results found that users were satisfied with the personalized healthy food recommendation system based on collaborative filtering and knapsack problem algorithm which included ability of operating system, screen design, and efficiency of operating system. The average satisfaction score overall was 4.20 implying that users had an excellent level of satisfaction
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