All posts by 370740cf

10k steps a day and maybe love along the way

A recent discussion with a friend inspired me to share my thoughts on the dating applications market. Many are available but none has yet found the secret recipe to instant love.

Dating apps are common among the 22-35 years old looking for the perfect date that may translate in something more (Digitaltrends, 2017). The most known options are of course Tinder, Happn and OkCupid. But many more exist such as Bumble, Tinder Select or even Innercircle all with features differentiating them from each other; either empowering women to make the first move, or allowing only high end profiles to find each other. As none of them can guarantee finding the perfect love, many dating applications options have developed. This has led to apps specializing in matching people based on a very specific preference or interest. An example of such a niche dating app is Sizzl, which connects people who like bacon. Many of such specific dating apps exist, check this out to see some examples.

A new dating app that I would like to talk about here, is Lime. This app has not launched yet but is expected to launch in the coming days (Digitaltrends, 2017). Their offering matches the digital health hype cycle (KPMG, 2015) where activity trackers are increasingly adopted by consumers hence their proposition may target a large proportion of the community.

Lime matches users based on their Apple’s health or Google’s fitness data. They believe that similar activity levels will mean similar interests. It works quite simply: when you start the app, you can see who is walking around in the neighborhood and look at their respective profiles (a little bit like Tinder). Simultaneously, you can keep track of your own activity level and hence might feel encouraged to walk towards one of the matching profiles. The added feature is that you can see the number of steps he or she is away. Additionally, to tracking your own step count and see how meeting with others may enhance this number, the app allows you to invite others to meet up based on the steps he or she is away and consequently increase your number of steps for the day. Whenever the other accepts your offer you can chat to decide on the exact location and directly meet to grab a coffee or else and of course see whether you are made for each other.

The app has not launched yet so we do not know whether it will be a success, however I have some thoughts about it.

I really think that the matching based on the activity of the user may be a nice feature as people who are highly active throughout the day may also need to find a significant other which can keep up with such a high activity level. Same holds for people who are less active. However, I have my concerns about the instant meeting up feature. In theory, it sounds cool but I am not sure whether in practice it would work.

Primarily, I think that users will not necessarily meet up instantly. It is possible to talk to each other without having to meet right away. This is undermining the concept of the application but will certainly happen as some people may feel that they need to change their clothes, or freshen up before meeting up. Hence, I do not believe that the steps feature will be so unique that people would want to switch from a dating app such as Tinder or Happn to this one proposed under the name Lime.



KPMG, 2015. How is Technology changing Healthcare? | KPMG. [ONLINE] Available at: [Accessed 10 March 2017].

Digital Trends, 2017. The 8 Best Dating Apps for 2017 | Digital Trends. [ONLINE] Available at: [Accessed 10 March 2017].

Lime – Hyper local lifestyle dating App. 2017. Lime – Hyper local lifestyle dating App. [ONLINE] Available at: [Accessed 10 March 2017].

Karissa Bell, 2017. Tinder is testing a secret version of the app for the rich, famous and hot. [ONLINE] Available at: [Accessed 10 March 2017].

A personal stylist: now accessible to everyone!

The online access to clothing has enabled the emergence of many new business models within the fashion industry. We see subscription models that allow the customer to rent clothing for a fixed period (Rent The Runway) or a platform connecting stylists with customers to get personal styling advice (Keaton Row). At Stitch Fix, which is a US based firm, the website offers its customers the expertise of a stylist to buy new products. You are required to sign up, give some background information on yourself, on the style that you like, the type of items you are interested in and your size. After that the stylist will pick out a selection of products and the customer just has to wait for the package to land at the door.

Initially this business model was developed to serve the young professionals or executives who do not have the time to go shopping in store or online. But it appears that also people with less busy agendas are interested in the service as it allows you to have the help of a stylist when choosing pieces amidst the high number of available products (Drell, 2014).

The large variety of products available online has made it hard to choose which piece to buy.

At Stitch Fix the stylist will, based on your preferences and requests, show you the items she has picked. You can then give feedback and decide which items you want to try at home. Once you receive the package at home you can try and keep or send back the items you finally do not like (free of charge). You can personalize your demands according to your needs, your budget and the brands you love.

The styling advice costs $20 but as soon as you buy the pieces offered you do not have to pay it. This makes the barrier to use the service very low as the service provided ends up free of charge. You only pay the price of each piece that you decide to buy.

Based on what you buy the stylist can improve her recommendations and hence understand your preferences better every time you place an order. The platform also allows you to schedule the frequency of the package delivery, making it possible to systematically buy new products at the requested interval.

The advantages are the convenience of a stylist helping you to choose the best product, at whatever time, for the budget you want to spend and only paying for what you really like and keep.

In comparison with business models that allow you to rent clothing at a fraction of the purchase price this business offers you still the possibility to own the pieces and not having to return them afterwards. Renting pieces is attractive when you are looking for a piece for a special occasion, a one-time event. Whereas Stitch Fix allows you to buy products you love for every day without having to spend time searching webpages or clothing stores to find it.

The supplier side of the business also encounters many advantages. Stitch Fix can create very precise consumer profiles based on the purchases they have made and the feedback they have given the stylist. This gives the company valuable knowledge about why certain products work better than others. And this can be of use to the clothing industry to better serve the customer’s needs. Whether Stitch Fix works closely with producers is not known but it could be an option to expand its revenue stream.

Stitch Fix does not deliver its services outside of the US but if you are interested in trying it out, a European alternative exists developed by Zalando, and called Zalon.




Drell, L, 2014, 17 Unique Business Models Shaking Up the Marketplace. [ONLINE] Available at: [Accessed 01 March 2017].

“The Filter Bubble: exploring the effects of using recommender systems on content diversity” (Nguyen et al. 2014)

The article chosen addresses the so-called bubble effect identified by Pariser (2012). This bubble effect suggests that by using recommender systems (RS), users are exposed to only a few products that they will like and miss out of many others.  The paper wants to investigate this through understanding the content diversity at an individual level provided by collaborative filtering. It suggests to be the first study observing the effects of this phenomenon on an individual level.

From the study conducted by Lee and Hosanagar (2015) we have understood that there are many opposing views existing in the literature on content diversity on an individual level. Therefore, as the current article claims to be the first one studying this phenomenon on an individual level it is interesting to see how the study has been conducted and what their conclusions are compared to the article of 2015.

The paper addresses very well the debate in regard of the bubble effect: whether recommender systems may be harmful to users. First the behavioral aspects of people who are exposed to similar content and what the effect is on their individual behavior is addressed. As they want to measure the effects on an individual level it is important to recognize what has been found in regard of individual behaviors on content exposure.

For this study, they use the long-term users of MovieLens as they need longitudinal data to draw conclusions on the user’s behavior over time. Two research questions are addressed; 1. Do recommender systems expose users to narrower content over time? 2. How does the experience of users using recommender systems differ of those who do not rely on recommender systems?

The article uses the “tag genome” developed by Vig et al. (2012) to analyze the diversity of the movies that are recommended and consumed (rated). This appears to be a strong measure as it identifies the content of the movie and identifies the similarities content-wise. Multiple articles have used the movie genres (Lee and Hosanagar, 2015) or the ratings given (Adamopoulos and Tuzhilin, 2014) to identify similarities which seems to be less generalizable as the content of the movies still can vary greatly when using these metrics.

The article describes clearly how the findings should be interpreted and addresses multiple questions that have risen based on the findings. This leads to a well-rounded study where the effect of item-item collaborative filtering is exposed. First, the article addresses whether recommender systems expose users to a narrower content over time through comparing the content diversity at the beginning with the content diversity at the end of the user’s rating history. This comparison can show the development of content consumption of the user over time. It has been found that the content diversity of both user groups (using RS or not to rate movies) becomes quite similar over time. Furthermore, it identifies whether using RS reduces the total content-diversity consumed of that user. The conclusion is that users using RS over time consume more diverse content then users ignoring RS. Finally, the experience of the two user groups are evaluated and it is observed that users using RS seem to consume more enjoyable movies based on their ratings given.

As the limitations of the article suggest itself it would be interesting to study the phenomenon in a more experimental setting where the behavior of users can be observed in more detail. This would help in understanding the reasons of the decisions made by the users based on the recommendations. The multiple studies conducted in the field of RS mostly focus on collaborative filtering as this RS is the most commonly used (Lee and Hosanagar, 2015) but research should also focus on other recommender systems to make sure that those used benefit the user the most.



Adamopoulos, P. and Tuzhilin, A., 2014, October. On over-specialization and concentration bias of recommendations: Probabilistic neighborhood selection in collaborative filtering systems. In Proceedings of the 8th ACM Conference on Recommender systems (pp. 153-160). ACM.

Lee, D. and Hosanagar, K., 2015. ‘People Who Liked This Study Also Liked’: An Empirical Investigation of the Impact of Recommender Systems on Sales Diversity.

Nguyen, T.T., Hui, P.M., Harper, F.M., Terveen, L. and Konstan, J.A., 2014, April. Exploring the filter bubble: the effect of using recommender systems on content diversity. In Proceedings of the 23rd international conference on World wide web (pp. 677-686). ACM.