BCRecommender Traction Update

This is the fifth part of a series of posts on my Bandcamp recommendations (BCRecommender) project.
Check out previous posts on the general motivation behind this project, the system’s architecture, the recommendation algorithms, and initial traction planning.

In a previous post, I discussed my plans to apply the Bullseye framework from the Traction Book to BCRecommender, my Bandcamp recommendations project. In that post, I reviewed the 19 traction channels described in the book, and decided to focus on the three most promising ones: blogger outreach, search engine optimisation (SEO), and content marketing. This post discusses my progress to date.


My initial traction goals were rather modest: get some feedback from real people, build up steady nonzero traffic to the site, and then increase that traffic to 10+ unique visitors per day. It’s worth noting that I have four other main areas of focus at the moment, so BCRecommender is not getting all the attention I could potentially give it. Nonetheless, I have made good progress on achieving my goals (first two have been obtained, but traffic still fluctuates), and learnt a lot in the process.

Things that worked

Blogger outreach. The most obvious people to contact are existing Bandcamp fans. It was straightforward to generate a list of prolific fans with blogs, as Bandcamp allows people to populate their profile with a short bio and links to their sites. I worked my way through part of the list, sending each fan an email introducing BCRecommender and asking for their feedback. Each email required some manual work, as the vast majority of people don’t have their email address listed on their Bandcamp profile page. I was careful not to be too spammy, which seemed to work: about 50% of the people I contacted visited BCRecommender, 20% responded with positive feedback, and 10% linked to BCRecommender in some form, with the largest volume of traffic coming from my Hypebot guest post. The problem with this approach is that it doesn’t scale, but the most valuable thing I got out of it was that people like the project and that there’s a real need for it.

Twitter. I’m not sure where Twitter falls as a traction channel. It’s probably somewhere between (micro)blogger outreach and content marketing. However you categorise Twitter, it has been working well as a source of traffic. Simply finding people who may be interested in BCRecommender and tweeting related content has proven to be a rather low-effort way of getting attention, which is great at this stage. I have a few ideas for driving more traffic from Twitter, which I will try as I go.

Things that didn’t work

Content marketing. I haven’t really spent time doing serious content marketing apart from the Spotlights pilot. My vision for the spotlights was to generate quality articles automatically and showcase music on Bandcamp in an engaging way that helps people discover new artists, even if they don’t have a fan account. However, full automation of the spotlight feature would require a lot of work, and I think that there are lower-hanging fruits that I should focus on first. For example, finding interesting insights in the data and presenting them in an engaging way may be a better content strategy, as it would be unique to BCRecommender. For the spotlights, partnering with bloggers to write the articles may be a better approach than automation.

SEO. I expected BCRecommender to rank higher for “bandcamp recommendations” by now, as a result of my blogger outreach efforts. At the moment, it’s still on the second page for this query on Google, though it’s the first result on Bing and DuckDuckGo. Obviously, “bandcamp recommendations” is not the only query worth ranking for, but it’s very relevant to BCRecommender, and not too competitive (half of the first page results are old forum posts). One encouraging outcome from the work done so far is that my Hypebot guest post does appear on the first page. Nonetheless, I’m still interested in getting more search engine traffic. Ranking higher would probably require adding more relevant content on the site and getting more quality links (basically what SEO is all about).

Points to improve and next steps

I could definitely do better work on all of the above channels. Contrary to what’s suggested by the Bullseye framework, I would like to put more effort into the channels that didn’t work well. The reason is that I think they didn’t work well because of lack of attention and weak experiments, rather than due to their unsuitability to BCRecommender.

As mentioned above, my main limiting factor is a lack of time to spend on the project. However, there’s no pressing need to hit certain traction milestones by a specific deadline. My stretch goals are to get all Bandcamp fans to check out the project (hundreds of thousands of people), and have a significant portion of them convert by signing up to updates (tens of thousands of people). Getting there will take time. So far I’m finding the process educational and enjoyable, which is a pleasant surprise.

Bandcamp recommendation and discovery algorithms

This is the third part of a series of posts on my Bandcamp recommendations (BCRecommender) project.
Check out the first part for the general motivation behind this project and the second part for the system architecture.

The main goal of the BCRecommender project is to help me find music I like. This post discusses the algorithmic approaches I took towards that goal. I’ve kept the descriptions at a fairly high-level, without getting too much into the maths, as all recommendation algorithms essentially try to model simple intuition. Please leave a comment if you feel like something needs to be explained further.

Data & evaluation approach

The data was collected from publicly-indexable Bandcamp fan and track/album (aka tralbum) pages. For each fan, it consists of the tralbum IDs they bought or wishlisted. For each tralbum, the saved data includes the type (track/album), URL, title, artist name, and the tags (as assigned by the artist).

At the moment, I have data for about 160K fans, 335K albums and 170K tracks. These fans have expressed their preference for tralbums through purchasing or wishlisting about 3.4M times. There are about 210K unique tags across the 505K tralbums, with the mean number of tags per tralbum being 7. These figures represent a fairly sparse dataset, which makes recommendation somewhat challenging. Perhaps this is why Bandcamp doesn’t do much algorithmic recommendation.

Before moving on to describe the recommendation approaches I played with, it is worth noting that at this stage, my way of evaluating the recommendations isn’t very rigorous. If I can easily find new music that I like, I’m happy. As such, offline evaluation approaches (e.g., some form of cross-validation) are unlikely to correlate well with my goal, so I just didn’t bother with them. Having more data would allow me to perform more rigorous online evaluation to see what makes other people happy with the recommendations.

Personalised recommendations with preferences (collaborative filtering)

My first crack at recommendation generation was using collaborative filtering. The broad idea behind collaborative filtering is using only the preference matrix to find patterns in the data, and generate recommendations accordingly. The preference matrix is defined to have a row for each user and a column for each item. Each matrix element value indicates the level of preference by the user for the item. To keep things simple, I used unary preference values, where the element that corresponds to user/fan u and item/tralbum i is set to 1 if the fan purchased or wishlisted the tralbum, or set to missing otherwise.

A simple example for collaborative filtering is in the following image, which was taken from the Wikipedia article on the topic.

Simple collaborative filtering example

I used matrix factorisation as the collaborative filtering algorithm. This algorithm was a key part of the winning team’s solution to the Netflix competition. Unsurprisingly, it didn’t work that well. The key issue is that there are 160K * (335K + 170K) = 80.8B possible preferences in the dataset, but only 3.4M (0.004%) preferences are given. What matrix factorisation tries to do is to predict the remaining 99.996% of preferences based on the tiny percentage of given data. This just didn’t yield any music recommendations I liked, even when I made the matrix denser by dropping fans and tralbums with few preferences. Therefore, I moved on to employing an algorithm that can use more data – the tags.

Personalised recommendations with tags and preferences (collaborative filtering and content-based hybrid)

Using data about the items is referred to as content-based recommendation in the literature. In the Bandcamp recommender case, the content data that is most easy to use is the tags that artists assign to their work. The idea is to build a profile for each fan based on tags for their tralbums, and recommend tralbums with tags that match the fan’s profile.

As mentioned above, the dataset contains 210K unique tags for 505K tralbums, which means that this representation of the dataset is also rather sparse. One obvious way of making it denser is by dropping rare tags. I also “tagged” each tralbum with a fan’s username if that fan purchased or wishlisted the tralbum. In addition to yielding a richer tralbum representation, this approach makes the recommendations likely to be less obvious than those based only on tags. For example, all tralbums tagged with rock are likely to be rock albums, but tralbums tagged with yanir are somewhat more varied.

To make the tralbum representation denser I used the latent Dirichlet allocation (LDA) implementation from the excellent gensim library. LDA assumes that there’s a fixed number of topics (distributions over tags, i.e., weighted lists of tags), and that every tralbum’s tags are generated from its topics. In practice, this magically yields clusters of tags and tralbums that can be used to generate recommendations. For example, the following word cloud presents the top tags in one cluster, which is focused on psychedelic-progressive rock. Each tralbum is assigned a probability of being generated from this cluster. This means that each tralbum is now represented as a probability distribution over a fixed number of topics – much denser than the raw tag data.

psychedelic-progressive-rock tag cloud

Using LDA for generating recommendations is straightforward, as each fan can be represented as the concatenation of the tags assigned to their tralbums, together with their own user tag. This representation is then converted to a topic distribution, which is compared to all the tralbums to yield the most similar ones.

This approach yielded much better results than collaborative filtering. I actually found albums I like and made some purchases, more than just the three that are annotated on my fan page (I didn’t want to be too spammy). Woohoo!

However, the problem with this approach is that it doesn’t take my mood into account, as it is based on my entire profile. To address this, I introduced similar music and cluster-based discovery.

Beyond static personalisation: similar music and cluster-based discovery

It is easy to see that the LDA-based tralbum representation makes it straightforward to calculate similarity between tralbums, and also explore tralbums that belong to the same topic/cluster. Adding this functionality to BCRecommender means that users can explore similar tralbums to a tralbum or a cluster in the style that they are interested in right now – based on their mood. Implementing these features helped me find more music I like, so again, I’m happy.

Tweaking the similarity algorithms is still a work in progress, as is finding a scalable way to generate useful cluster/spotlight pages. However, my focus now (in the time that I can allocate to working on this project) is on getting some people using it and iterate following their feedback.

Future extensions

It would be awesome to make BCRecommender’s discovery process smoother. For example, it’d be fairly straightforward to just stream all the recommendations rather than making users click album by album (like Pandora, Spotify, etc.). Iterating on the above approaches to improve the user experience is also likely to yield good results.

However, as mentioned above, my current focus is on getting more people to use BCRecommender. While the target audience is rather small, it doesn’t matter because I’m not trying to make money from this project. I am certain that many fans would discover new music using the website. At this stage, I just need to get them to visit, which is something that I will write about in future posts.

Building a Bandcamp recommender system (part 1 – motivation)

I’ve been a Bandcamp user for a few years now. I love the fact that they pay out a significant share of the revenue directly to the artists, unlike other services. In addition, despite the fact that fans may stream all the music for free and even easily rip it, almost $80M were paid out to artists through Bandcamp to date (including almost $3M in the last month) – serving as strong evidence that the traditional music industry’s fight against piracy is a waste of resources and time.

One thing I’ve been struggling with since starting to use Bandcamp is the discovery of new music. Originally (in 2011), I used the browse-by-tag feature, but it is often too broad to find music that I like. A newer feature is the Discoverinator, which is meant to emulate the experience of browsing through covers at a record store – sadly, I could never find much stuff I liked using that method. Last year, Bandcamp announced Bandcamp for fans, which includes the ability to wishlist items and discover new music by stalking/following other fans. In addition, they released a mobile app, which made the music purchased on Bandcamp much easier to access.

All these new features definitely increased my engagement and helped me find more stuff to listen to, but I still feel that Bandcamp music discovery could be much better. Specifically, I would love to be served personalised recommendations and be able to browse music that is similar to specific tracks and albums that I like. Rather than waiting for Bandcamp to implement these features, I decided to do it myself. Visit BCRecommender – Bandcamp recommendations based on your fan account to see where this effort stands at the moment.

While BCRecommender has already helped me discover new music to add to my collection, building it gave me many more ideas on how it can be improved, so it’s definitely a work in progress. I’ll probably tinker with the underlying algorithms as I go, so recommendations may occasionally seem weird (but this always seems to be the case with recommender systems in the real world). In subsequent posts I’ll discuss some of the technical details and where I’d like to take this project.

It’s probably worth noting that BCRecommender is not associated with or endorsed by Bandcamp, but I doubt they would mind since it was built using publicly-available information, and is full of links to buy the music back on their site.