Context-aware Youtube Recommender System Manzar Abbas1 , Muhammad Usman Riaz1 , Asad Rauf1 , Muhammad Taimoor Khan1 and Shehzad Khalid2 Email: {p136119, p136063, p136070, taimoor.khan}@nu.edu.pk Department of Computer Science, FAST-NUCES Peshawar 25000, Pakistan Email: shehzad
[email protected] 2 Department of Computer Engineering, Bahria University Islamabad 44000, Pakistan 1
Abstract—Youtube is one of the most popular video sharing online resource that has millions of users around the world. The huge bulk of videos, which are growing at a high rate is posing problems for users to traverse through to relevant content. Users are facilitated with recommended videos that appeal to there interests. Following a hybrid recommendation approach, videos are recommended based on both collaborative recommendation and content-based recommendation. A limitation associated to this approach is that the videos recommended may not necessarily be appropriate to the current context that the user is in. Its very common for a single user to follow different interests depending of on the context they are in. A context-aware recommender system is proposed for Youtube that keeps track of multiple interests of a user and recommends videos based on their current context only. It serves a user better in finding relevant videos and has higher relevance to human judgment. Keywords: Recommendation systems, label propagation, collaborative filtering, random walks, video search, opinion mining, context relevance
I. I NTRODUCTION Youtube is the most popular video sharing resource that has millions of users since launched in 2005. It has the largest video archive and is growing at an astonishing rate with seven hours of videos being transferred each moment [1]. This colossal vault of video data can possibly contain videos of enthusiasm for some clients [2]. The drawback to the amount of videos is that investigation and disclosure of new and fascinating videos becomes an overwhelming task. The standard recommendation approaches are content-based recommendation, collaborative recommendation and a mix of both as hybrid recommendation approach [1]. Recently, Youtube follows hybrid recommendation approach that is implemented through deep learning neural networks [3]. It plays a very important role in enticing the recommended videos to play as they mostly address to the interests of a user. A search field is also provided to have users look for specific content, in case they are sure what they want to get. In order to give explicit feedback on the videos, the users can like, dislike and comment on them [4]. Videos watch time and watching them completely or not etc. are the implicit feedbacks. This information plays a vital role in the recommendation of a video. Hybrid recommendation systems consider both the content watched by the user in the past and the content that is watched by the other users with similar profiles. In collaborative recommendation systems, the profiles of users are compared for the nature of content they watch and the feedback given to
it. People with similar profiles are considered to be following similar interests and are recommended videos from each other’s profiles [1]. In case of content based recommendation, the user is recommended more content based on the kind of content they have watched or preferred in the past. The title and description of a video are important to evaluate a video for recommendation to a user. The ones with higher similarity are considered of interest to the user and are recommended. There are two possible activities for users, as they interact with their videos of interest i.e., explicit and implicit activities. Explicit activities include video rating, like or dislike, comment, subscription etc. Whereas, implicit activities consists of watching videos fully or partially without giving any opinion about it [5]. Hybrid recommender systems combines the content and collaborative recommendation approaches. Contextaware recommender systems is another popular approach to recommendation that filters the recommended videos from the above approaches with a context filter. So that the users are recommended only those relevant videos that matches their current context. This is a very effective approach as a single user pursues different interest in different contexts and would like them to be handled separately [6].
There are issues associated with the existing hybrid approach for recommending videos on Youtube. The videos recommended on Youtube follows multiple user interests. These interests are based on the kind of context that the user is in. Thus, a user who listen to music in evening would only want to be recommended songs in the evening. Similarly, the same user watching tutorials in the morning would appreciate tutorials recommended in the morning. Moreover, a user would listen to different type of music on week days and weekends. Therefore, it is very important for a user to be recommended only the kind of content that they would prefer in the given context. The existing approach fails to address this issue and recommend videos from multiple interests of the user, all together [7]. In this research a context-aware video recommender system is proposed, that keeps track of the user interests and only recommend content that comply with the current context. It keeps track of the multiple interests that the user follows. An application is developed that get data through Youtube API and recommend contextually related videos ordered in relevance to the user preferences.
II. L ITERATURE R EVIEW Recommender systems have been popularly used for various tasks in recent times [8]. They provide an effective mechanism for finding relevant content from huge volume of content that is pouring in continuously [9]. Youtube has been one such platform that has a very high rate of video uploads and viewership. Finding relevant content has been a concern for its users who turn to video based solutions for various purposes ranging from education to entertainment, medical, technical and household techniques [1]. It facilitates its users to easily locate videos of interests through a recommendation mechanism. If a video is played, the sidebar shows videos that are relevant to the video currently playing. When a search string is presented, the videos relevant to it are listed in descending order of their ranking. User profiles are associated to their Gmail accounts that maintain their activities for video recommendation [10]. In case of users who are new or not logged in, generally popular videos in recent times are recommended. A hybrid recommender system is a combination of both content based and collaborative recommender systems. The content on Youtube can be specified by the video’s title, description and related keywords. Video comments is a strong aspect that has been ignored by the existing video recommender systems [1]. Other user activities are also incorporated in recommendation as collaborative recommender systems [4]. Recommender systems have been used for recommending other types of content as well. In context aware commendation, the basic attributes that are related to the users or their environment are used to split a single user’s activities into various groups. Each group is represented by a context. The context represents the physical or mental state that the user is currently in. Different parameters can be used to represent the context of a user or the item that is being accessed [11]. The basic purpose of the context-aware recommender systems is to convert a 2D recommender system into a 3D recommender system. Collaborative recommender systems can be represented as [11], U ser × Item = Rating,
(1)
which is improved by context-aware recommender system as, U ser × Item × Context = Rating.
(2)
Knowledge-based recommender systems are used to recommend content to users within the constraints of explicit knowledge [7]. Knowledge is usually in the form of rules that defines the recommendation criteria [8], [12]. These rules can be about users, the items to recommend or any factors from the environment as context [13]. This information can be provided by domain experts by following a semi-supervised approach [14]. These rules can also be extracted automatically with the help of a learning mechanism in automatic knowledge based systems [15]. A major advantage of knowledge-based recommender system is their effectiveness to cold-start when
their isn’t enough samples to make reasonable recommendations. However, the potential knowledge acquisition is a concern as its explicit annotation will is expensive and time consuming while automatic extraction of rules can be manipulated by noisy rules. Generally knowledge-based recommender systems are well suited for complex domains having fewer associations. The context aware recommender systems restrict the recommendation to the current context giving them higher ranking score as compared to other items [9]. Thus if users with similar profiles have rated a video high in different contexts will not be recommended due to difference in contexts. It is because only a context based recommendation can address to the real needs of the users. It is very common for users have multiple interests that they follow in their specific contexts and would want them to be maintained separately. The existing Youtube recommender system doesn’t consider this user context. Context-aware recommendation systems have been used for recommending other types of content related to commercial products. Incorporating such context into Youtube’s recommender system would add to the comfort of end users in locating relevant content [6]. III. P ROPOSED M ODEL A context-aware recommender system is proposed for Youtube that is used on top of the hybrid recommender system in-built with it. It requests Youtube through its public API to get a list of highly recommended videos for a user. These videos are usually recommended based on the activities of the user in recent past. They are consumed by the contextaware recommender system as input. It identifies the current context and split the videos into two groups i.e. the videos that belong to the current context and the videos that does not belong to the current context. The videos that does not belong to the current context are filtered out. Therefore, the user is only presented with videos that coincides to their activities in the current context. The proposed system has its own ranking mechanism that order the recommended videos in decreasing order of their desirability for the end user. For this purpose it make use of meta information about the videos that is also acquired through the Youtube public API. A formula is devised using this information to rank videos. This formula also consider activities in the same context for ranking a video. The proposed system addresses a very important aspect of the recommendation regarding the current situation of the user. This has been overlooked by the existing systems while recommending videos. Working of the proposed system is highlighted in Algorithm 1. It may be provided with a Search query as input, in line 1. The output of the system is an ordered list of videos that are relevant to the current context and are ordered on the basis of their relevance, in line 2. Line 3, acquires videos from Youtube using its public API. The videos may be retrieved against Search query, otherwise generally popular videos are recommended. These videos are obtained by using the hybrid recommender system that is inbuilt to Youtube. The user’s
Algorithm 1 Algorithm of the proposed context-aware Youtube recommender system 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12:
Input: Search query Output: ordered context relevant videos videos[] = YoutubeSearchAPI(Search query = φ) currentContext = getContext(userProfile) for each video in videos do meta = acquiteMeta(video) if activity(meta) ≈ currentContext then filter else rankVideo end if end for
current context is obtained from their profile and other system properties. It currently has three attributes i.e. user’s age and current weekday and daytime. In lines 5 to 12, each video is processed against the current context of the user and is filtered or ranked accordingly. The meta information about the video is obtained, in line 6. In lines 7 to 11, the video is either filtered out if it isn’t in relevance to the current context of the user or ranked accordingly. It depends on the desirability of the video in the current context, is evaluated through its meta information. The context of a user is highlighted by three attributes that are currently considered in the proposed model. These attributes are age, daytime and weekday. They are categorical attributes each having limited possible values. The age of a user may be child or adult. Similarly, it could be week day or weekend and the daytime could be day or night. Based on this, there could be a total of eight different contexts that a user may be in at an instant. More attributes and more values to each attribute can be considered for more refined recommendations. A single user may be in different contexts at different times. Each time the videos are recommended to a user, their current context is considered. The recommendations that are not in accordance with the current context of the user have low likelihood of being visited. Therefore they are considered as noise even when they a user with similar profile has positive feedback for it. The context tree is shown inFigure 1 that has 8 possible sets. A user can be in one of these possible states and is considered while recommending videos. A recommender system is initially trained on a sample data. During this phase the user preferences in the form of meta information are stored along with the context state. For example, when a video is liked, the system stores the user with profile X and the video with details Y is liked in given Z. One such possible context can be {child, week day, day time}. Different user accounts were observed in the system to provide initial training data. The users were allowed to perform normal activities on the system as they would do on Youtube. They would watch videos fully or partially and may give explicit feedback in the form of likes, dislikes, commented and their channels subscribed. The prediction score for a video is generated with the help of this training data. It provides a starting point to the model. However, the videos rated in the
Fig. 1. Breakdown of the context with three attributes each having two values forming a total of eight possible contexts
Fig. 2. A comparison of most relevant videos in different contexts with precision@20
training phase are only a very small proportion of the videos hosted on Youtube. Therefore, a rating prediction mechanism is used to predict rating for an unrated video based on their similarity or differences to the videos that are rated in the training phase. It allows the system to predict ratings for millions of videos hosted on Youtube, based on their distance using Eucleadian distance, dist[v1 , v2 ] =
p w1 d2 (u, u0 ) + w2 d2 (i, i0 ) + w3 d2 (t, t0 ) (3)
between two items with attributes u, i, t and u’, i’, t’ respectively. It is a weighted distance that has weights w1 , w2 and w3 associated with attributes u, i and t, respectively. IV. E XPERIMENT AND R ESULTS The proposed system is experimented to evaluate its effectiveness and compare the results with the existing approach. Users are allowed to log in to their profiles in different contexts and rate the appropriateness of the videos recommended. Similarly, the videos recommended by Youtube are also evaluated by experts based on if the user would prefer to watch them. The videos are ordered according to the relevance that the user would be interested in watching them. Difference in the two types of video listing proposed by Youtube and our proposed
TABLE I S EARCH RESULTS FOR KEYWORD Panama BY YOUTUBE AND THE PROPOSED MODEL
Video ID XwG53kkqOQE 34Q5GpbldZg uqYWl4q16BE MSXqc t8yMs
Title PANAMA DECISION ... Panama Leaks JIT: ... 24 News HD Honest Officers Will ... Haroon Rasheed Telling ...
Youtube Video ID XwG53kkqOQE 34Q5GpbldZg uqYWl4q16BE MSXqc t8yMs
Likes 483 66 211 523
Dislikes 114 5 41 30
Date Of Upload Apr 20, 2017 May 3, 2017 May 5, 2017 Apr 22, 2017
Proposed Model Video ID KXKPY2ehCrE MSXqc t8yMs uqYWl4q16BE 34Q5GpbldZg
Likes 562 523 211 66
Dislikes 8 30 41 5
Date Of Upload May 2, 2017 Apr 22, 2017 May 5, 2017 May 3, 2017
for recommending appropriate videos to users. However, it misses the context which is very important in improving the experience of end users. The proposed context-ware Youtube video recommender system, stands on top of Youtube, internally getting data from Youtube through its API. However, it maintains its own context specific history of user activities against their profiles to suggest more appropriate videos. Human judges decided in favor of the proposed system for recommending more appropriate videos and in better ordering of quality as compared to the existing approach. This research work is currently in progress and in future results and analysis from different perspectives can be shared. One possible direction is to consider sentiment analysis of user comments and consider them the recommendation formula. However, handling the informal nature of content and the variety in languages will be a concern. R EFERENCES
system suggest that there could be different approaches to recommendation to serve the user needs in a better way. The proposed system observes the user needs more thoroughly and therefore propose fewer and specific videos with high tendency of being watched. Thus it improves user experience that would enhance the utilization of content on Youtube. Please note that the proposed system is not an alternate to Youtube and is rather built on top of Youtube to have refined recommendations by adjusting to the needs of users in various contexts. It cannot be used as a standalone system and is an extention of Youtube that operates on its API in the background. The objective of this research is to improve user’s experience with Youtube content by providing content that conforms to their interests. Therefore the results are evaluated by human judges who are familiar with the searched keywords. They watched the content of the recommended videos and compared them to their ordering to subjectively evaluate the model. Since the number of videos that could be enough to evaluate the appropriateness of recommendation and their ordering is not known, therefore, the natural way to evaluate these recommendations is to use Precision @ n (or p@n) for n= 10 and 20 videos. Figure 2 gives a comparison of the proposed system with Youtube based on the top 20 videos for four different contexts. It can be observed that the proposed system has outperformed the existing recommender system by Youtube. Improvement in the recommendation system is also elaborated through a comparison on the videos searched against Panama keyword. The video titles along with their structural information are shown in the Table I. For the sake of simplifying visualization, only top 5 recommendations are considered. V. C ONCLUSION Popular online platforms like Youtube have grown into huge data warehouses. In such high volume of data and competitiveness among similar platforms, the ones that efficiently and effectively facilitate their users are going to retain them for longer. Youtube follows a hybrid recommender approach
[1] S. Baluja, R. Seth, D. Sivakumar, Y. Jing, J. Yagnik, S. Kumar, D. Ravichandran, and M. Aly, “Video suggestion and discovery for youtube: taking random walks through the view graph,” in Proceedings of the 17th international conference on World Wide Web, pp. 895–904, ACM, 2008. [2] A. Krishna, J. Zambreno, and S. Krishnan, “Polarity trend analysis of public sentiment on youtube,” in Proceedings of the 19th International Conference on Management of Data, pp. 125–128, Computer Society of India, 2013. [3] M. Eirinaki and M. Vazirgiannis, “Web mining for web personalization,” ACM Transactions on Internet Technology (TOIT), vol. 3, no. 1, pp. 1– 27, 2003. [4] S. Siersdorfer, S. Chelaru, W. Nejdl, and J. San Pedro, “How useful are your comments?: analyzing and predicting youtube comments and comment ratings,” in Proceedings of the 19th international conference on World wide web, pp. 891–900, ACM, 2010. [5] M. Pazzani and D. Billsus, “Content-based recommendation systems,” The adaptive web, pp. 325–341, 2007. [6] J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, et al., “The youtube video recommendation system,” in Proceedings of the fourth ACM conference on Recommender systems, pp. 293–296, ACM, 2010. [7] M. I. Malik, M. J. Majeed, M. T. Khan, and S. Khalid, “Self-refining targeted readings recommender system,” in Robotics and Artificial Intelligence (ICRAI), 2016 2nd International Conference on, pp. 5–10, IEEE, 2016. [8] M. T. Khan and S. Khalid, “Multimodal rule transfer into automatic knowledge based topic models,” in Multi-Topic Conference (INMIC), 2016 19th International, pp. 1–6, IEEE, 2016. [9] G. Adomavicius and A. Tuzhilin, “Context-aware recommender systems,” in Recommender systems handbook, pp. 191–226, Springer, 2015. [10] P. Covington, J. Adams, and E. Sargin, “Deep neural networks for youtube recommendations,” in Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191–198, ACM, 2016. [11] S. Choudhury and J. G. Breslin, “User sentiment detection: a youtube use case,” 2010. [12] M. T. Khan, S. Yar, and S. Khalid, “Histogram based rule verification in lifelong learning models,” in Multi-Topic Conference (INMIC), 2016 19th International, pp. 1–5, IEEE, 2016. [13] M. T. Khan, S. Yar, S. Khalid, and F. Aziz, “Evolving long-term dependency rules in lifelong learning models,” in Knowledge Engineering and Applications (ICKEA), IEEE International Conference on, pp. 93– 97, IEEE, 2016. [14] M. J. Majeed, M. M. Malik, M. T. Khan, and S. Khalid, “Real-time government policy monitoring framework using expert guided topic modeling,” in Innovative Computing Technology (INTECH), 2016 Sixth International Conference on, pp. 47–51, IEEE, 2016. [15] M. T. Khan, N. Azam, S. Khalid, and J. Yao, “A three-way approach for learning rules in automatic knowledge-based topic models,” International Journal of Approximate Reasoning, vol. 82, pp. 210–226, 2017.
