Mobile Multimedia Recommendation in Smart Communities: A Survey <s> E. Innovative Mobile Multimedia Recommendation Paradigms 1) Proactive and Sensor-Based Recommendations <s> Research tools critical for exploratory search success involve the creation of new interfaces that move the process beyond predictable fact retrieval. <s> BIB001 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> E. Innovative Mobile Multimedia Recommendation Paradigms 1) Proactive and Sensor-Based Recommendations <s> This paper presents a multi-agent recommender system in which agents collaborate with each other to facilitate in providing travel recommendations. A set of rules are organized to be used as knowledge-base that enables travel agents to act in an intelligent and pro-active way. The agents are also in charge of building and updating profiles of travelers. History of the travelers' selections is kept using a multidimensional rating approach. Maximum-likelihood probability and multi-attribute theory are used to get the recommendation ratings and store them for future recommendations. The effectiveness of the system was evaluated by a diverse set of users with a variety of scenarios. The overall results indicate that the system is capable of pro-actively providing the required types of services with a good degree of accuracy. <s> BIB002
Traditional recommender systems usually require a user to submit query explicitly. A mobile recommender system that proactively pushes just-in-time personalized multimedia contents to mobile device users based on their contextual information as well as interests and preferences will not only solve the problem of information overload but also improve recommendation access in mobile multimedia as well as reduce keystrokes by mobile device users. Most mobile multimedia recommender systems wait for a user's request before delivering any recommendation. Proactive and sensor-based recommendations will support a more exploratory approach to information search BIB001  . In proactive and sensor-based recommendations, the recommender system just detects how the user is accessing and using the system, for example browsing and reading habits. Therefore, minimal input is asked from the user, but none of the reviewed systems is capable of proactively interrupting the user's activity with unsolicited but relevant recommendations. Some researchers BIB002 -  have however recently developed mobile multimedia recommender systems that generate proactive and sensor-based recommendations to some extent. The new generation of inexpensive and reliable biometric sensors can make this type of recommendations possible and convenient. This can revolutionize the role of mobile multimedia recommender systems from topic oriented multimedia information seeking and decision making tools to multimedia information discovery and entertaining companions.