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id="nav-single"> <h3 class="assistive-text">Post navigation</h3> <span class="nav-previous"><a href="https://code.flickr.net/2016/05/11/we-want-you-and-your-teammates/" rel="prev"><span class="meta-nav">←</span> Previous</a></span> <span class="nav-next"><a href="https://code.flickr.net/2017/01/05/a-year-without-a-byte/" rel="next">Next <span class="meta-nav">→</span></a></span> </nav> <article id="post-3453" class="post-3453 post type-post status-publish format-standard hentry category-hadoop category-infrastructure tag-personalization"> <header class="entry-header"> <h1 class="entry-title">Personalized Group Recommendations on Flickr</h1> <div class="entry-meta"> <span class="sep">Posted on </span><a href="https://code.flickr.net/2016/09/30/personalized-group-recommendations-on-flickr/" title="4:04 am" rel="bookmark"><time class="entry-date" datetime="2016-09-30T04:04:10-07:00">September 30, 2016</time></a><span class="by-author"> <span class="sep"> by </span> <span class="author vcard"><a class="url fn n" href="https://code.flickr.net/author/mehulpatel001/" title="View all posts by Mehul Patel" rel="author">Mehul Patel</a></span></span> </div> </header> <div class="entry-content"> <p><span style="font-weight:400;">There are two primary paradigms for the discovery of digital content. First is the search paradigm, in which the user is actively looking for specific content using search terms and filters (e.g., Google </span><a href="https://www.google.com/?q=iceland"><span style="font-weight:400;">web search</span></a><span style="font-weight:400;">, Flickr </span><a href="https://www.flickr.com/search/?text=iceland&dimension_search_mode=min&height=1024&width=1024"><span style="font-weight:400;">image search</span></a><span style="font-weight:400;">, Yelp </span><a href="https://www.yelp.com/search?find_desc=irish+pub&find_loc=San+Francisco,+CA&start=0&attrs=RestaurantsPriceRange2.2&open_now=4277"><span style="font-weight:400;">restaurant search</span></a><span style="font-weight:400;">, etc.). Second is a passive approach, in which the user browses content presented to them (e.g., NYTimes </span><a href="http://www.nytimes.com/"><span style="font-weight:400;">news</span></a><span style="font-weight:400;">, Flickr </span><a href="https://www.flickr.com/explore"><span style="font-weight:400;">Explore</span></a><span style="font-weight:400;">, and Twitter </span><a href="https://twitter.com/trendingtopics/"><span style="font-weight:400;">trending topics</span></a><span style="font-weight:400;">). Personalization benefits both approaches by providing relevant content that is tailored to users’ tastes (e.g., Google </span><a href="https://news.google.com/"><span style="font-weight:400;">News</span></a><span style="font-weight:400;">, Netflix </span><a href="https://www.netflix.com/browse"><span style="font-weight:400;">homepage</span></a><span style="font-weight:400;">, LinkedIn </span><a href="https://www.linkedin.com"><span style="font-weight:400;">job search</span></a><span style="font-weight:400;">, etc.). We believe personalization can improve the user experience at Flickr by guiding both new as well as more experienced members as they explore photography. Today, we’re excited to bring you personalized group recommendations.</span></p> <p><span style="font-weight:400;">Flickr Groups are great for bringing people together around a common theme, be it a style of photography, camera, place, event, topic, or just some fun. Community members join for several reasons—to consume photos, to get feedback, to play games, to get more views, or to start a discussion about photos, cameras, life or the universe. We see value in connecting people with appropriate groups based on their interests. Hence, we decided to start the personalization journey by providing contextually relevant and personalized content that is tuned to each person’s unique taste. </span></p> <p><span style="font-weight:400;">Of course, in order to respect users’ privacy, group recommendations only consider public photos and public groups. Additionally, recommendations are private to the user. In other words, nobody else sees what is recommended to an individual. </span></p> <p><span style="font-weight:400;">In this post we describe how we are improving Flickr’s group recommendations. In particular, we describe how we are replacing a curated, non-personalized, static list of groups with a dynamic group recommendation engine that automatically generates new results based on user interactions to provide personalized recommendations unique to each person. The algorithms and backend systems we are building are broad and applicable to other scenarios, such as photo recommendations, contact recommendations, content discovery, etc.</span></p> <p><img fetchpriority="high" decoding="async" class=" size-full wp-image-3463 aligncenter" src="https://wp.flickr.net/wp-content/uploads/sites/3/2016/09/group_recommendations2.png" alt="Group_recommendations2.png" width="2188" height="1924" srcset="https://code.flickr.net/wp-content/uploads/sites/3/2016/09/group_recommendations2.png 2188w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/group_recommendations2.png?resize=150,132 150w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/group_recommendations2.png?resize=800,703 800w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/group_recommendations2.png?resize=768,675 768w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/group_recommendations2.png?resize=1024,900 1024w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/group_recommendations2.png?resize=1536,1351 1536w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/group_recommendations2.png?resize=2048,1801 2048w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/group_recommendations2.png?resize=341,300 341w" sizes="(max-width: 2188px) 100vw, 2188px" /></p> <p style="text-align:center;"><b>Figure</b><span style="font-weight:400;">: Personalized group recommendations</span></p> <h1>Challenges</h1> <p><span style="font-weight:400;">One challenge of recommendations is determining a user’s interests. These interests could be user-specified, explicit preferences or could be inferred implicitly from their actions, supported by user feedback. For example: </span></p> <ul> <li style="font-weight:400;"><span style="font-weight:400;">Explicit:</span> <ul> <li style="font-weight:400;"><span style="font-weight:400;">Ask users what topics interest them</span></li> <li style="font-weight:400;"><span style="font-weight:400;">Ask users why they joined a particular group</span></li> </ul> </li> <li style="font-weight:400;"><span style="font-weight:400;">Implicit:</span> <ul> <li style="font-weight:400;"><span style="font-weight:400;">Infer user tastes from groups they join, photos they like, and users they follow</span></li> <li style="font-weight:400;"><span style="font-weight:400;">Infer why users joined a particular group based on their activity, interactions, and dwell time</span></li> </ul> </li> <li style="font-weight:400;"><span style="font-weight:400;">Feedback:</span> <ul> <li style="font-weight:400;"><span style="font-weight:400;">Get feedback on recommended items when users perform actions such as “Join” or “Follow” or click “Not interested”</span></li> </ul> </li> </ul> <p><span style="font-weight:400;">Another challenge of recommendations is figuring out group characteristics. I.e.: what type of group is it? What interests does it serve? What brings Flickr members to this group? We can infer this by analyzing group members, photos posted to the group, discussions and amount of activity in the group.</span></p> <p><span style="font-weight:400;">Once we have figured out user preferences and group characteristics, recommendations essentially becomes a matchmaking process. At a high-level, we want to support 3 use cases:</span></p> <ul> <li style="font-weight:400;"><b>Use Case # 1</b><span style="font-weight:400;">: Given a group, return all groups that are “similar”</span></li> <li style="font-weight:400;"><b>Use Case # 2</b><span style="font-weight:400;">: Given a user, return a list of recommended groups</span></li> <li style="font-weight:400;"><b>Use Case # 3</b><span style="font-weight:400;">: Given a photo, return a list of groups that the photo could belong to</span></li> </ul> <h1>Collaborative Filtering</h1> <p><span style="font-weight:400;">One approach to recommender systems is presenting similar content in the current context of actions. For example, Amazon’s “Customers who bought this item also bought” or LinkedIn’s “People also viewed.” Item-based collaborative filtering can be used for computing similar items.</span></p> <p><img decoding="async" class="alignnone wp-image-3457 aligncenter" src="https://wp.flickr.net/wp-content/uploads/sites/3/2016/09/collaborative_filtering.gif" alt="collaborative_filtering" width="463" height="447" /></p> <p style="text-align:center;"><b>Figure</b><span style="font-weight:400;">: Collaborative filtering in action</span></p> <p style="text-align:center;"><span style="font-weight:400;">By Moshanin (Own work) [</span><a href="http://creativecommons.org/licenses/by-sa/3.0"><span style="font-weight:400;">CC BY-SA 3.0</span></a><span style="font-weight:400;">] from </span><a href="https://upload.wikimedia.org/wikipedia/commons/5/52/Collaborative_filtering.gif"><span style="font-weight:400;">Wikipedia</span></a></p> <p><span style="font-weight:400;">Intuitively, two groups are similar if they have the same content or same set of users. We observed that users often post the same photo to multiple groups. So, to begin, we compute group similarity based on a photo’s presence in multiple groups. </span></p> <p style="text-align:left;"><span style="font-weight:400;">Consider the following sample matrix </span><span style="font-weight:400;">M</span><span style="font-weight:400;">(</span><span style="font-weight:400;">G</span><span style="font-weight:400;">i</span><span style="font-weight:400;"> -> </span><span style="font-weight:400;">P</span><span style="font-weight:400;">j</span><span style="font-weight:400;">) constructed from group photo pools, where 1 means a corresponding group (</span><span style="font-weight:400;">G</span><span style="font-weight:400;">i</span><span style="font-weight:400;">) contains an image, and empty (0) means a group does not contain the image.</span></p> <p style="text-align:left;"><img decoding="async" class=" size-full wp-image-3472 aligncenter" src="https://wp.flickr.net/wp-content/uploads/sites/3/2016/09/matrix1.png" alt="matrix1" width="569" height="237" srcset="https://code.flickr.net/wp-content/uploads/sites/3/2016/09/matrix1.png 569w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/matrix1.png?resize=150,62 150w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/matrix1.png?resize=500,208 500w" sizes="(max-width: 569px) 100vw, 569px" /></p> <p style="text-align:left;"><span style="font-weight:400;">From this, we can compute </span><span style="font-weight:400;">M.</span><span style="font-weight:400;">M’</span> <span style="font-weight:400;">(</span><span style="font-weight:400;">M</span><span style="font-weight:400;">’s </span><a href="https://en.wikipedia.org/wiki/Transpose"><span style="font-weight:400;">transpose</span></a><span style="font-weight:400;">), which gives us the number of common photos between every pair of groups (G</span><span style="font-weight:400;">i</span><span style="font-weight:400;">, G</span><span style="font-weight:400;">j</span><span style="font-weight:400;">):</span></p> <p style="text-align:left;"><img loading="lazy" decoding="async" class="size-full wp-image-3535 aligncenter" src="https://wp.flickr.net/wp-content/uploads/sites/3/2016/09/matrix21.png" alt="matrix2" width="512" height="182" srcset="https://code.flickr.net/wp-content/uploads/sites/3/2016/09/matrix21.png 512w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/matrix21.png?resize=150,53 150w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/matrix21.png?resize=500,178 500w" sizes="auto, (max-width: 512px) 100vw, 512px" /></p> <p><span style="font-weight:400;">We use modified </span><a href="https://en.wikipedia.org/wiki/Cosine_similarity"><span style="font-weight:400;">cosine similarity</span></a><span style="font-weight:400;"> to compute a similarity score between every pair of groups: </span></p> <p style="text-align:center;"><img loading="lazy" decoding="async" class="alignnone size-full wp-image-3536" src="https://wp.flickr.net/wp-content/uploads/sites/3/2016/09/cosinesimilarity1.png" alt="cosinesimilarity" width="337" height="59" srcset="https://code.flickr.net/wp-content/uploads/sites/3/2016/09/cosinesimilarity1.png 337w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/cosinesimilarity1.png?resize=150,26 150w" sizes="auto, (max-width: 337px) 100vw, 337px" /></p> <p><span style="font-weight:400;">To make this calculation robust, we only consider groups that have a minimum of X photos and keep only strong relationships (i.e., groups that have at least Y common photos). Finally, we use the similarity scores to come up with the top k-nearest neighbors for each group. </span></p> <p><span style="font-weight:400;">We also compute group similarity based on group membership —i.e., by defining group-user relationship (G</span><span style="font-weight:400;">i</span><span style="font-weight:400;"> -> U</span><span style="font-weight:400;">j</span><span style="font-weight:400;">) matrix. It is interesting to note that the results obtained from this relationship are very different compared to (G</span><span style="font-weight:400;">i</span><span style="font-weight:400;">, P</span><span style="font-weight:400;">j</span><span style="font-weight:400;">) matrix. The group-photo relationship tends to capture groups that are similar by content (e.g.,“macro photography”). On the other hand, the group-user relationship gives us groups that the same users have joined but are possibly about very different topics, thus providing us with a diversity of results. We can extend this approach by computing group similarity using other features and relationships (e.g., autotags of photos to cluster groups by themes, geotags of photos to cluster groups by place, frequency of discussion to cluster groups by interaction model, etc.).</span></p> <p><span style="font-weight:400;">Using this we can easily come up with a list of similar groups (Use Case # 1). We can either merge the results obtained by different similarity relationships into a single result set, or keep them separate to power features like “Other groups similar to this group” and “People who joined this group also joined.”</span></p> <p><span style="font-weight:400;">We can also use the same data for recommending groups to users (Use Case # 2). We can look at all the groups that the user has already joined and recommend groups similar to those. </span></p> <p><span style="font-weight:400;">To come up with a list of relevant groups for a photo (Use Case # 3), we can compute photo similarity either by using a similar approach as above or by using Flickr computer vision models for finding photos similar to the query photo. A simple approach would then be to recommend groups that these similar photos belong to.</span></p> <h1>Implementation</h1> <p><span style="font-weight:400;">Due to the massive scale (millions of users x 100k groups) of data, we used </span><a href="http://yahoohadoop.tumblr.com/"><span style="font-weight:400;">Yahoo’s Hadoop Stack</span></a><span style="font-weight:400;"> to implement the collaborative filtering algorithm. We exploited sparsity of entity-item relationship matrices to come up with a more efficient model of computation and used several optimizations for computational efficiency. We only need to compute the similarity model once every 7 days, since signals change slowly. </span></p> <p><img loading="lazy" decoding="async" class=" wp-image-3537 aligncenter" src="https://wp.flickr.net/wp-content/uploads/sites/3/2016/09/architecture_diagram1.jpg" alt="architecture_diagram" width="507" height="383" /></p> <p style="text-align:center;"><b>Figure</b><span style="font-weight:400;">: Computational architecture</span></p> <p style="text-align:center;"><span style="font-weight:400;">(All logos and icons are trademarks of respective entities)</span></p> <p> </p> <p><span style="font-weight:400;">Similarity scores and top k-nearest neighbors for each group are published to </span><a href="http://redis.io/"><span style="font-weight:400;">Redis</span></a><span style="font-weight:400;"> for quick lookups needed by the serving layer. Recommendations for each user are computed in real-time when the user visits the </span><a href="https://www.flickr.com/groups"><span style="font-weight:400;">groups</span></a><span style="font-weight:400;"> page. Implementation of the serving layer takes care of a few aspects that are important from usability and performance point-of-view:</span></p> <ul> <li style="font-weight:400;"><b>Freshness of results</b><span style="font-weight:400;">: Users hate to see the same results being offered even though they might be relevant. We have implemented a randomization scheme that returns fresh results every X hours, while making sure that results stay static over a user’s single session.</span></li> <li style="font-weight:400;"><b>Diversity of results</b><span style="font-weight:400;">: Diversity of results in recommendations is very important since a user might not want to join a group that is very similar to a group he’s already involved in. We require a good threshold that balances similarity and diversity. To improve diversity further, we combine recommendations from different algorithms. We also cluster the user’s groups into diverse sets before computing recommendations.</span></li> <li style="font-weight:400;"><b>Dynamic results</b><span style="font-weight:400;">: Users expect their interactions to have a quick effect on recommendations. We thus incorporate user interactions while making subsequent recommendations so that the system feels dynamic.</span></li> <li style="font-weight:400;"><b>Performance</b><span style="font-weight:400;">: Recommendation results are cached so that API response is quick on subsequent visits. </span></li> </ul> <h1>Cold Start</h1> <p><span style="font-weight:400;">The drawback to collaborative filtering is that it cannot offer recommendations to new users who do not have any associations. For these users, we plan to recommend groups from an algorithmically computed list of top/trending groups alongside manual curation. As users interact with the system by joining groups, the recommendations become more personalized.</span></p> <h1>Measuring Effectiveness</h1> <p><span style="font-weight:400;">We use qualitative feedback from user studies and alpha group testing to understand user expectation and to guide initial feature design. However, for continued algorithmic improvements, we need an objective quantitative metric. Recommendation results by their very nature are subjective, so measuring effectiveness is tricky. The usual approach taken is to roll out to a random population of users and measure the outcome of interest for the test group as compared to the control group (ref: </span><a href="https://en.wikipedia.org/wiki/A/B_testing"><span style="font-weight:400;">A/B testing</span></a><span style="font-weight:400;">). </span></p> <p><span style="font-weight:400;">We plan to employ this technique and measure user interaction and engagement to keep improving the recommendation algorithms. Additionally, we plan to measure explicit signals such as when users click “Not interested.” This feedback will also be used to fine-tune future recommendations for users.</span></p> <p><img loading="lazy" decoding="async" class="size-medium wp-image-3538 aligncenter" src="https://wp.flickr.net/wp-content/uploads/sites/3/2016/09/measuringeffectiveness1.png?w=800" alt="measuringeffectiveness" width="800" height="349" srcset="https://code.flickr.net/wp-content/uploads/sites/3/2016/09/measuringeffectiveness1.png 2110w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/measuringeffectiveness1.png?resize=150,65 150w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/measuringeffectiveness1.png?resize=800,349 800w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/measuringeffectiveness1.png?resize=768,335 768w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/measuringeffectiveness1.png?resize=1024,446 1024w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/measuringeffectiveness1.png?resize=1536,670 1536w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/measuringeffectiveness1.png?resize=2048,893 2048w, https://code.flickr.net/wp-content/uploads/sites/3/2016/09/measuringeffectiveness1.png?resize=500,218 500w" sizes="auto, (max-width: 800px) 100vw, 800px" /></p> <p style="text-align:center;"><b>Figure</b><span style="font-weight:400;">: Measuring user engagement</span></p> <h1>Future Directions</h1> <p><span style="font-weight:400;">While we’re seeing good initial results, we’d like to continue improving the algorithms to provide better results to the Flickr community. Potential future directions can be classified broadly into 3 buckets: algorithmic improvements, new product use cases, and new recommendation applications.</span></p> <p><i><span style="font-weight:400;">If you’d like to help, we’re hiring. Check out our </span></i><a href="https://www.flickr.com/jobs"><i><span style="font-weight:400;">jobs page</span></i></a><i><span style="font-weight:400;"> and get in touch.</span></i></p> <p><b><i>Product Engineering</i></b><i><span style="font-weight:400;">: Mehul Patel, Chenfan (Frank) Sun, Chinmay Kini</span></i></p> </div> <footer class="entry-meta"> This entry was posted in <a href="https://code.flickr.net/category/hadoop/" rel="category tag">hadoop</a>, <a href="https://code.flickr.net/category/infrastructure/" rel="category tag">infrastructure</a> and tagged <a href="https://code.flickr.net/tag/personalization/" rel="tag">personalization</a> by <a href="https://code.flickr.net/author/mehulpatel001/">Mehul Patel</a>. Bookmark the <a href="https://code.flickr.net/2016/09/30/personalized-group-recommendations-on-flickr/" title="Permalink to Personalized Group Recommendations on Flickr" rel="bookmark">permalink</a>. <div id="author-info"> <div id="author-avatar"> <img alt='' src='https://secure.gravatar.com/avatar/a2964ff46b4e23e1183384089d260c87?s=68&d=identicon&r=g' srcset='https://secure.gravatar.com/avatar/a2964ff46b4e23e1183384089d260c87?s=136&d=identicon&r=g 2x' class='avatar avatar-68 photo' height='68' width='68' loading='lazy' decoding='async'/> </div> <div id="author-description"> <h2> About Mehul Patel </h2> Mehul is the lead backend engineer at Flickr. He is interested in photography, data science and personalization - especially a combination of all 3 to bring interesting features to Flickr community. <div id="author-link"> <a href="https://code.flickr.net/author/mehulpatel001/" rel="author"> View all posts by Mehul Patel <span class="meta-nav">→</span> </a> </div> </div> </div> </footer> </article> <div id="comments"> </div> </div> </div> </div> <footer id="colophon" role="contentinfo"> <div id="site-generator"> © 2024 Flickr, Inc. 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