This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements.
N-screen (multidevices) spurred the research in this field of study in order to provide a mechanism to share and move the content among devices having different screen sizes, resolutions, and network access interfaces [
The explosion of communication technologies and multimedia-capable devices is influencing the ways in which users expect to interact with the content. The heterogeneous nature of device attributes and access network conditions present new questions in the realm of user experience with the content and quality of service [
Recommender systems (RSs) are invaluable tools to help users in handling information overload, as the number of items (e.g., products, news, movies, and music) is growing exponentially. RSs are software applications providing suggestions for items to be of use to a user using either his history of interests, contextual information, social interactions, or his behavior similarities to other users. The suggestions are based on different decision making algorithms, which turn the user preferences into predictions of possible future user likes and interests. The computational approach for a recommendation is to select the objects favored by other users that are similar to the target user’s profile. Typically, RSs compare some characteristics of users’ profiles or items to find similar users or items and predict the rating that the active user would assign to an item. The existing RSs find similar users and use their watched content as a candidate for recommendations. The existing systems had an issue that one user may like content at night but due to the similarity with another user the system can recommend it in the daytime. We consider temporal information with user ratings to find which user likes which content at which time.
Due to exponential growth and development in data generating sources, RSs are entering into every field of life to help users get useful information from an overabundance of data. Social networks like
We organize the paper as follows. Section
The research closely related to our work is conventional recommendation and personalization systems of online movies or videos on demand. The central parts of the RSs are the recommendation algorithms, and based on recommendation algorithms they can be classified as collaborative filtering [
Collaborative filtering (CF) depends on user opinions such as rating information. CF methods store enough information and use this for finding similarities with the target user and recommend an item based on the similarity. Most of the CF algorithms consider that a sufficient collection of users or items profiles is available and it recommends items, matching to user profiles. In other words, the CF considers the user interests and profile instead of item’s attributes and content. The CF algorithms are of three types: memory based [
Content-based filtering (CBF) recommends items to the active users based on the content similarity with what users watched or experienced before. Unlike CF, the CBF algorithm built a profile by analyzing the contents of the items that active user had rated in the past. The content-based recommender systems discover semantically related contents and keywords. The main problem with these algorithms is that they need structured information and they are not useful for unstructured data such as videos, audios, and images.
Most of the RSs get a single value for user preference about an item, which represents the preference of user
CF has problems of cold start issues and sparsity, while CBF needs structured information as well as having cold start issues. In order to solve these issues, a hybrid approach is used. The hybrid RSs combine CF with the CBF or any other information such as demographics. Hybrid RSs merge two or more recommendation techniques to improve the accuracy and combine the predictions obtained from different methods either by a simple linear combination [
We can summarize the contribution of our research in the field of recommendation as follows. (1) It can be applied in any field of recommendation, but it is highly applicable in the field of online movies or programs recommendation because it recommends the contents by considering the user device’s static and dynamic profile. (2) It uses temporal information to find out which user likes which content at which time, for example, if a user likes short length videos during his office time and drama and family movies with his family at night time. (3) We use a hybrid unified model that incorporates the user rating information, demographics, and users’ N-screen information. (4) The proposed individual feature and subspace weight based clustering produces good quality of clusters as compared to simple
The heterogeneity of devices and access network conditions necessitate an N-screen content aware recommendation system in order to ensure that the user N-screen device and access network are capable of streaming and displaying with the user’s preferences. This section presents the architecture of the proposed N-screen aware multicriteria hybrid recommender system. Figure
Architecture of N-screen aware multicriteria hybrid recommender system.
Schema for user N-screen device profile attributes.
Protocol for the acquisition of user’s N-screen device profile.
In conventional RSs, the systems use either the users similarity or items similarity, and based on this they can recommend the content that a user watched at night to another user at the daytime. However, the user’s preferences and streaming content display devices change with the time of the day; for example, at night the user prefers to watch drama with his family at large screen but at the office he prefers to watch short video clips on his smart phone. To improve the accuracy of N-screen aware recommendation and user experience with content, we introduce the temporal aware usage history and divided the time of the day in four temporal ranges. In the recommendation architecture with N-screen information and temporal awareness, we introduce individual feature and subspace weight based clustering (IFSWC) to assign a different weight to each subspace, that is, user preferences, N-screen information and user demographics, and a weight to each feature within a subspace.
This section presents our proposed scheme of N-screen aware recommendation. Figure
Algorithm/flow chart of N-screen aware multicriteria hybrid recommender system using weight based subspace clustering.
We characterized the user profile by his preferences on an item having multirating, demographics, and N-screen device profile. The user provides his demographics information and number of N-screen devices during service registration. We acquire the user device profile as we described in Section
A unified hybrid framework is used that incorporates the user temporal aware preferences with his demographics and N-screen information. The framework provides the following advantages. (1) Using temporal information, it considers only effective users at a specific time range. (2) The user rating has a smaller size considering only effective items and temporal information to minimize the computational complexity, so it requires less time to find similar users compared to the traditional collaborative filtering. (3) Since the model incorporates and fuses three different sources of data, we propose IFSWC to assign different weights to each feature and subspace to improve the clustering accuracy.
This paper suggests an IFSWC to assign different weights to each subspace and individual feature to improve the clustering quality. For effectiveness and computational efficiency, we cluster similar users considering temporal information, that is, specific time range. Since our algorithm fuses different sources of data, we embed the weight mechanism in the
Let
(1) Select (2) Assign initial feature value to each subgroup (
(3) Assign the user user co-rated programs multi-rating, N-Screen info. of these experienced programs and the user demographics similarities. (4) Update the clusters center of user multi-rating and N-Screen information by using ( and the demographics information by ( (5) Calculate the adjustment margins for individual feature and subgroup by using ( (6) Adjust the weights using (
The N-screen aware recommendation ensures that the user’s N-screen device and access network are capable of displaying and streaming. In the proposed architecture, the NCS maintains the user’s N-screen device attribute repository and the active status of the device. The system acquires the user’s N-screen device dynamic profile and ensures that the user’s N-screen is already registered and its static profile is in the NCS. Based on the active status of the user’s N-screen, we find the dynamic profile of each of these devices. To recommend the content to the user’s N-screen, we retrieve the active user experienced programs with the N-screen information of the active devices from the user data. In this data, we replace the dynamic profile attributes with the current dynamic attributes of the device and access network. Although we can provide a recommendation to the user on multiple active N-screen devices, the scope of this work is to consider a single active N-screen device. The following steps are involved in the recommendation phase: (1) selecting the cluster of the highest similarity with the active user, finding users from that cluster having higher similarity with the active user, and using their programs as a candidate for recommendation, (2) finding a prediction about an item to select the candidate items for recommendations, and (3) selecting the top-
As in Section
Clusters center
(1) If the target user has a previous history of uncompleted programs, and he changed his device, check the device’s similarity with the previous one if capable, recommend the same program or else GoTo Step 2 (2) Select the cluster with higher similarity with the active user using (i) And lets assume (3) Find the user-user similarity of the target user to the cluster users using (ii)
We consider items of the top-
After selection and finding predictions on candidate items for recommendation, we have to select the top-
To evaluate the effectiveness of the proposed recommender system, we use the multicriteria movies data collected from Yahoo Movies! [
The most commonly used metric for finding the accuracy of a recommender system is the MAE. The MAE finds the deviation of predictions on items generated by recommender system from the true rating values, and having low MAE value means better prediction and recommendation accuracy. If
Comparison of MAE performance measure.
In information retrieval, precision and recall are the most popular metrics for evaluation of the system. Precision means the exactness and recall means the completeness [
We compute the precision and recall by considering the top-20 nearest neighbor users to the target user by recommending the items (
Performance comparison of precision/recall.
This paper proposes a new recommender system for user N-screen aware recommendations that incorporates the user N-screen device attributes like screen size, access network speed, and remaining battery time and temporal aware usage information that are not considered in previous recommender systems. The proposed system guarantees that a user can watch the content on different devices, at different times, and in different locations with his preferences. The proposed recommender system introduces the UDPCA, UDPCM, and NCS to acquire the user N-screen devices static profile and dynamic profile using RESTful URIs, and it maintains the N-screen repository and device active status for N-screen aware recommendations. Unlike the conventional recommender systems which can recommend the content the user watched at night time to similar users during office hours, we introduce temporal information to find out which user likes which content at which time to improve the precision, recall, and scalability. The hybrid framework incorporates and fuses various data about the user to enhance the sparsity and cold start issues; that is, the proposed system considers users similarities of content multirating, N-screen attributes, and demographics. Finally, good results in terms of accuracy and precision/recall are achieved by using individual feature and subspace weight based clustering and temporal information. The proposed recommender system can be applied in any field of recommendation, but it is highly applicable in the field of IPTV programs personalization or online movies recommendation because it considers the user N-screen devices profile with its preferences and demographics.
Number of users
Number of clusters
Users corated items/programs
Number of features (multirating, N-screen information, and demographics)
Cluster membership function
Number of subspaces, that is, subgroups
Weight of a feature
Weight of a subspace
Centroid, that is, cluster center
User information vector (multirating and N-screen information)
User demographics
Cluster user information vector (multirating and N-screen information)
Cluster user demographics
Separation (distance) within cluster objects
Separation (distance) between clusters centroids
Global mean value
Number of users in cluster
Iteration for cluster objective function converage
Target/active user rating and N-screen information
Target/active user demographics
Average rating of target user.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research is supported by the Industrial Strategic Technology Development Program (Development of SmartTV Device Collaborated Open Middleware and Remote User Interface Technology for N-Screen Service) funded by the Ministry of Knowledge Economy (MKE), Republic of Korea (Grant no. 10039202).